{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction\n",
    "\n",
    "Link/edge prediction demo for cora dataset (homegeneous network) where all nodes are papers and edges between nodes are citation links, e.g., paper A cites paper B. \n",
    "\n",
    "Each paper has a **subject** attribute with one of 7 values denoting the subject area of the paper.\n",
    "\n",
    "This demo notebook **demonstrates how to predict citation links/edges between papers** using the random walk-based representation learning method Node2Vec.\n",
    "\n",
    "<a name=\"refs\"></a>\n",
    "**References:** \n",
    "\n",
    "[1] Node2Vec: Scalable Feature Learning for Networks. A. Grover, J. Leskovec. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2016. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from math import isclose\n",
    "from sklearn.decomposition import PCA\n",
    "import os\n",
    "import networkx as nx\n",
    "import numpy as np\n",
    "from stellargraph.data import EdgeSplitter\n",
    "from utils.cl_arguments_parser import parse_args\n",
    "from utils.read_graph import read_graph\n",
    "from utils.predictors import *\n",
    "from collections import Counter\n",
    "import multiprocessing\n",
    "from stellargraph import datasets\n",
    "from IPython.display import display, HTML\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Default parameters for Node2Vec\n",
    "parameters = {\n",
    "    \"p\": 1.0,  # Parameter p\n",
    "    \"q\": 1.0,  # Parameter q\n",
    "    \"dimensions\": 128,  # dimensionality of node2vec embeddings\n",
    "    \"num_walks\": 10,  # Number of walks from each node\n",
    "    \"walk_length\": 80,  # Walk length\n",
    "    \"window_size\": 10,  # Context size for word2vec\n",
    "    \"iter\": 1,  # number of SGD iterations (epochs)\n",
    "    \"workers\": multiprocessing.cpu_count(),  # number of workers for word2vec\n",
    "    \"weighted\": False,  # is graph weighted?\n",
    "    \"directed\": False,  # are edges directed?\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "The Cora dataset consists of 2708 scientific publications classified into one of seven classes. The citation network consists of 5429 links. Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary. The dictionary consists of 1433 unique words."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cora dataset is already downloaded\n",
      "Graph statistics: 2708 nodes, 5429 edges\n",
      "Largest subgraph statistics: 2485 nodes, 5069 edges\n"
     ]
    }
   ],
   "source": [
    "dataset = datasets.Cora()\n",
    "display(HTML(dataset.description))\n",
    "dataset.download()\n",
    "\n",
    "cora_location = os.path.join(dataset.data_directory, \"cora.cites\")\n",
    "dataset_name = \"cora\"\n",
    "# Special handling: Cora lists edges in 'cited-paper' <- 'citing-paper' order\n",
    "g_nx = read_graph(\n",
    "    graph_file=cora_location, dataset_name=dataset_name, is_directed=True\n",
    ").reverse()\n",
    "g_nx = g_nx.to_undirected()\n",
    "\n",
    "# Select the largest connected component. For clarity we ignore isolated\n",
    "# nodes and subgraphs; having these in the data does not prevent the\n",
    "# algorithm from running and producing valid results.\n",
    "g_nx_ccs = (g_nx.subgraph(c).copy() for c in nx.connected_components(g_nx))\n",
    "g_nx = max(g_nx_ccs, key=len)\n",
    "print(\n",
    "    \"Largest subgraph statistics: {} nodes, {} edges\".format(\n",
    "        g_nx.number_of_nodes(), g_nx.number_of_edges()\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Construct train and test splits of the input data\n",
    "\n",
    "We are going to tackle link prediction as a supervised learning problem, where we are going to train a binary classifier to predict a link, or not, between any two nodes in the graph. \n",
    "\n",
    "For supervised classification, we need a training set of positive and negative examples of links and a corresponding **train** graph with those positive links removed; this graph is used for representation learning.\n",
    "\n",
    "For evaluation, we will use a hold out dataset, that is a set of positive and negative examples of links, and a corresponding **test** graph with those positive links removed.\n",
    "\n",
    "We are going to split our input graph into a **train** and **test** graphs using the EdgeSplitter class in stellar.data.edge_splitter. We are going to use the **train** graph for training the predictive model (a binary classifier that given two nodes it will predict whether a link between these two nodes should exist or not) and the **test** graph for evaluating the predictive model's performance on hold out data. \n",
    "\n",
    "Each graph will have the same number of nodes as the input graph but the number of edges will differ as some of the edges will be removed during each split and used as the positive samples for training the link prediction classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "** Sampled 506 positive and 506 negative edges. **\n",
      "** Sampled 456 positive and 456 negative edges. **\n"
     ]
    }
   ],
   "source": [
    "# Test graph and edge test data\n",
    "edge_splitter_test = EdgeSplitter(g_nx)\n",
    "g_test, edge_data_ids_test, edge_data_labels_test = edge_splitter_test.train_test_split(\n",
    "    p=0.1, method=\"global\"\n",
    ")\n",
    "\n",
    "# Train graph and edge train data\n",
    "edge_splitter_train = EdgeSplitter(g_test, g_nx)\n",
    "(\n",
    "    g_train,\n",
    "    edge_data_ids_train,\n",
    "    edge_data_labels_train,\n",
    ") = edge_splitter_train.train_test_split(p=0.1, method=\"global\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert graphs\n",
    "\n",
    "The node2vec reference implementation requires that the given graph is undirected and homogeneous. We cast the **train** and **test** graphs accordingly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train and Test graphs should be of type nx.Graph\n",
    "g_test = nx.Graph(g_test)\n",
    "g_train = nx.Graph(g_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the link prediction model and evaluate on test data\n",
    "\n",
    "We are going to train a link prediction classifier in three steps. \n",
    "1. We use Node2Vec [[1]](#refs), to calculate node embeddings. These embeddings are learned in such a way to ensure that nodes that are close in the graph remain close in the embedding space.\n",
    "2. We calculate link/edge embeddings for the positive and negative edge samples by applying a binary operator on the embeddings of the source and target nodes of each sampled edge. We consider 4 different operators: Hadamard; $L_1$; $L_2$; and *average* - the paper [[1]](#refs) provides a detailed description of these operators. All operators produce link embeddings that have equal dimensionality to the input node embeddings (128 dimensions for our example.) \n",
    "3. Given the embeddings of the positive and negative examples, we train a logistic regression classifier to predict a binary value indicating whether an edge between two nodes should exist or not.\n",
    "4. We evaluate the performance of the link classifier for each of the 4 operators on the training data and select the best performing one. The selected operator is used to predict the hold out, test, data with node embeddings calculated on the **test** graph.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning 128-dimensional node features (embeddings) from g_train using node2vec algorithm with the following parameters:\n",
      "\tp = 1.0, q = 1.0\n",
      "\tnum_walks = 10, walk length = 80, context window size = 10\n",
      "(Node2VecFeatureLearning) Time for random walks 7 seconds\n",
      "(Node2VecFeatureLearning) Time for learning embeddings 7 seconds.\n",
      "Total time for fit() 13.972423315048218 seconds\n",
      "Training binary link classifiers (link predictors) using the following binary operators: ['h', 'avg', 'l1', 'l2']\n",
      "Operator: h Score (ROC AUC on test set of edge_data): 0.883\n",
      "Operator: avg Score (ROC AUC on test set of edge_data): 0.607\n",
      "Operator: l1 Score (ROC AUC on test set of edge_data): 0.867\n",
      "Operator: l2 Score (ROC AUC on test set of edge_data): 0.897\n",
      "Learning 128-dimensional node features (embeddings) from g_test using node2vec algorithm with the following parameters:\n",
      "\tp = 1.0, q = 1.0\n",
      "\tnum_walks = 10, walk length = 80, context window size = 10\n",
      "(Node2VecFeatureLearning) Time for random walks 7 seconds\n",
      "(Node2VecFeatureLearning) Time for learning embeddings 7 seconds.\n",
      "Total time for fit() 14.316356897354126 seconds\n",
      "Evaluating best link predictor with l2 binary operator on test links in g_test\n",
      "Prediction score (ROC AUC): 0.9210775047258979\n",
      "\n",
      "  **** Scores on test set of links ****\n",
      "\n",
      "     Operator: l2  Score (ROC AUC): 0.92\n",
      "\n",
      "  ****************************\n"
     ]
    }
   ],
   "source": [
    "train_fl, test_fl, clf_edge = train_homogeneous_graph(\n",
    "    g_train=g_train,\n",
    "    g_test=g_test,\n",
    "    output_node_features=\"embeddings.emb\",\n",
    "    edge_data_ids_train=edge_data_ids_train,\n",
    "    edge_data_labels_train=edge_data_labels_train,\n",
    "    edge_data_ids_test=edge_data_ids_test,\n",
    "    edge_data_labels_test=edge_data_labels_test,\n",
    "    parameters=parameters,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualise representations of link embeddings\n",
    "\n",
    "Learned link embeddings have 128 dimensions but for visualisation we project them down to 2 dimensions using the PCA algorithm ([link](https://en.wikipedia.org/wiki/Principal_component_analysis)). \n",
    "\n",
    "Green points represent positive edges and red points represent negative (no edge should exist between the corresponding vertices) edges."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a41f9a390>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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A+JIx5hXLsgbz/ayl3jsDAAAAhwwP1zeY3m9mxq6k5qLxEgAHMPQXAAAAlaHxEoAaIagCAACgMrWaJwug4RFUAQAAUJlyGi8BQBmYowoAAIDKuT1PFkAgUVEFAADBwrqeAOB7BFUAABAcrOsJAIFAUAUAAMExOiqFQva6nsbYt6GQvd1rqPwCQEEEVQAAEBwzM/Y6nrm8uK5nuZXfakMtoRiAzxBUAQBAcPhlXc9yKr/VDmdmODQAHyKoAgCA4PDLup7lVH6rHc7sp+HQAHAPQRUAAASHX9b1LKfyW+1wZr8MhwaAHKyjCgAAgsUP63qOjNjDbyU7NKZShSu/sZg9XLera3tbOcOZq308ALiAiioAAEC9lVP5rXY4s1+GQwNADmNZltv7kNfg4KA1OTnp9m4AAAC4b3zcnlM6O2tXQkdGyqsaV/v4KkzNT2liekLxZFxHw0d14vgJDRwcqMtrA/A2Y8wrlmUN5v0ZQRUAAAC1MDU/pXMXzynaEVWkLaLEWkILKws6+eRJ58NqNozPzNjDnesYxgFUplhQZegvAAAAamJiekLRjqi627vVZJrU3d6taEdUE9MTzr4QS/AAgUNQBQAAQE3Ek3FF2iI7tkXaIrqevO7sC7EEDxA4BFUAAADUxNHwUSXWEju2JdYS6g33OvtCLMEDBA5BFQAAADVx4vgJLawsaGl1SRkro6XVJS2sLOjE8RPbdxofl4aG7HmlQ0OVDdctZ11aAL5AUAUAAEBNDBwc0MknTyocCiueiCscCu9spOTU3FKW4AECh66/AAAA2KleHXSHhuxw2tW1vW152V5X9vz58p7LxSV4AFSG5WkAAABQmmyVMxSy53mmUnZ18uxZ54NfLGZXUo3Z3mZZ0uKiND3t7GsB8ByWpwEAAEBp6tlBl7mlAAogqAIAAGBbPTvoMrcUQAGOBFVjzNeMMTeNMT8s8HNjjPl9Y8wVY8wlY8zHnHhdAEDAONH9E0B16lnlHB62hxQfOWIP9z1ypDZDjAH4jlMV1T+W9NNFfj4saeDen89LGnPodQEAQeFU988gIsCjnupd5RwethsnTU/bt4RUAHIoqFqW9V1JC0Xu8nOS/sSy/WdJ3caYI068NgAgIOo5L85PGinAE8i9gSonAA+o1xzVo5Ley/n7tXvbdjDGfN4YM2mMmbx161addg0A4An1nBfnJ40S4BspkPsBVU4ALqtXUDV5tu1aF8eyrK9aljVoWdbgAw88UIfdAgB4Bt0/82uUAN8ogRwAUJJ6BdVrko7l/L1P0vU6vTYAwA/o/plfowT4egVyhhfnx3EB4DH1CqrPS/rsve6//0jSHcuybtTptQEAfsC8uPwaJcDXI5AzvDg/jgsADzKWtWsEbvlPYsw3JH1C0iFJc5J+S1KrJFmW9UfGGCPprOzOwClJn7Msa7LYcw4ODlqTk0XvAgBAYxgft4fAzs7awW1kJHgBPhuWQiG7kppK2YHcyYsVQ0N2COvq2t62vGxfFDl/3pnX8COOCwCXGGNesSxrMO/PnAiqtUBQBQCgwdQ6kMdidsXQ5LTOsCy7gj897dzr+A3HBYBLigXVlnrvDAAAQF7Dw7WtFMdiuyuHQZzvWy6OCwAPqtccVQAAAHc1ynzfcnFcAHgQQRUAADQGGnblx3EB4EHMUQUAAAAA1F2xOapUVAEACBrWxAQA+BxBFQCAIGFNTOTiogUAnyKoAgAQJKOj9lqkXV32ciNdXfbfR0fd3jPUGxctAPgYQRUAgCCZmZE6O3du6+y01yZFY+GiBQAfI6gCABAksZi9BmYu1sRsTFy0AOBjBFUAAIKENTGDrZw5p1y0AOBjBFUAAILE6TUxacbjHeXOOeWiBQAfYx1VAADcMj5uzxecmbGD4MhI5YGyFrLBKBSyh4ymUnbQqSb4onJDQ3Y47era3ra8bF+MOH8+/2Oy37HZWbuS6rXvGICGVmwdVYIqAABu8EMIrCQYoXZiMbuSasz2NsuyK+fT0+7tFwBUqFhQZegvAABu8ENHVprxeAtzTgE0EIIqAABuqEcIrHZ+KcHIW5hzCqCBEFQBAHBDrUNguY138iEYeYvTjbIAwMMIqgAAuKHWIdCJocUEI+8ZHrbnB09P27d8FmgkdCFvKDRTAgDALbXsyErjHQBB4ocGdChbsWZKLfXeGQAAcM/wcO1OsGKx3R17mV8KwK9yR4lI27ejowTVgGLoLwAAQXT/0OK5Obty+w//wJA5FMbQSngVXcgbDkEVAIAgyp1fGo9Lt29Lhw5JfX2VNVZC8DnRgAuoFbqQNxzmqAIA4CNT81OamJ5QPBnX0fBRnTh+QgMHB4o/aGho9zDg5WU7xJ4/X9sdhn/wPYGXMUc1kIrNUaWiCgCAT0zNT+ncxXNKppPqi/QpmU7q3MVzmpqfKv5Ahsw5L4hDZPmewMvoQt5wCKoAAPjExPSEoh1Rdbd3q8k0qbu9W9GOqCamJ4o/kCFzzgrqENl6fE+CGPBRPyzP1FAIqgAA+EQ8GVekLbJjW6QtouvJ68UfWOs1WxuNE2vUelGtvydBDfgAaoKgCgCATxwNH1ViLbFjW2Itod5wb/EHMmTOWUEdIlvr70lQAz6AmmAdVQAAfOLE8RM6d/GcJLuSmlhLaGFlQc88+szeD67lmq2NJshr1NbyezIzY1dScwUh4AOoCSqqAAD4xMDBAZ188qTCobDiibjCobBOPnly766/cBZDqSvDXGkAZaCiCgCAjwwcHKhfMB0ft4dlzszYIWNkhKqstD1EdnTUrgb293NsSjEyYs9JlXYuL0LAB5AH66gCAIDdWLMQtZC9+EHAB6Di66gSVAEAwG5DQ7vnYS4v2w12zp93b78AAIFRLKgyRxUAAOwW1M62AABfIKgCAIDdaHwDAHARQRUAAOxGZ1sAgIsIqgAAlGJ83J63GYvZt+Pjbu9RbWU72x45Ii0u2rc0UgIA1AnNlAAA2AsdcAEAcBzNlAAAqMboqB1Su7okY+zbUMje3ggarZoMAHAdQRUAgL00cgfcbDX5xg0pGrVvT5+uX1glJFeG4wbA5wiqAADspZE74LpZTXY7JPsVxw1AABBUAQDYSyN3wHWzmtzoQ64rxXEDEAAEVQBAYT4ePjg1P6WxC2M6c/6Mxi6MaWp+qvIna+QOuG5Wkxt5yHU1OG4AAoCgCgDIz8fDB6fmp3Tu4jkl00n1RfqUTCd17uK56sPq+fPS9LR92wghVXK3mtzIQ66rwXEDEAAEVQBAfj4ePjgxPaFoR1Td7d1qMk3qbu9WtCOqiekJt3fNf9ysJjfykOtqcNwABABBFQCQn4+HD8aTcUXaIju2Rdoiup687tIe+Zxb1eRGHnJdDY4bgABocXsHAAAeFYvZw327ura3+WT44NHwUSXWEupu797allhLqDfc6+JeoSLDwwSsSgTpuI2P2yM5Zmbsf5dGRoLz3gAUREUVAJCfj4cPnjh+QgsrC1paXVLGymhpdUkLKws6cfyE27sGoBw+nisPoDoEVQBAfj4ePjhwcEAnnzypcCiseCKucCisk0+e1MDBAbd3DfAXtzt/+3iuPIDqGMuy3N6HvAYHB63JyUm3dwMAAKAxZauZoZA9Pz2VskdV1POCVSxmV1KN2d5mWfbFs+np+uwDgJoxxrxiWdZgvp9RUQUA+Jfb1R4gyLxQzWSpHaBhEVQBAJ4xNT+lsQtjOnP+jMYujBVf95S5a0BteaHzt4/nygOoDkEVAOAJU/NTOnfxnJLppPoifUqmkzp38VzhsOqFag8QZF6oZvp4rjyA6hBUAQCeMDE9oWhHVN3t3WoyTepu71a0I6qJ6Yn8D/BCtSdIGEaN+3mlmunWOr4AXEVQBQB4QjwZV6QtsmNbpC2i68nr+R/ghWpPULgxjJpg7H1UMwG4iKAKAPCEo+GjSqwldmxLrCXUG+7N/wCvVHuCoN7DqJlf7B9UMwG4hKAKAPCEE8dPaGFlQUurS8pYGS2tLmlhZUEnjp/I/wCqPc6p9zBq5hd7UlnNzACgxlhHFQDgGVPzU5qYntD15HX1hnt14vgJDRwccHu3gm9oyK5qdnVtb1tetsP/+fPOvx5rY3pOtplZtCOqSFtEibWEFlYWdPLJk/wOAqiZYuuottR7ZwAAKGTg4AAnxW4YGbGH3kp2JTWVqu0w6lhsdzBmfrGrcpuZSdq6nZie4HcSgCsY+gsAQKOr9zBq5he7o0gDq7KbmfkdzbwAzyOoAgBQDT+d8Bbb13o2zWF+cf3t0cCq7GZmfvalL0k///PS974n3b4tTU3RzAvwIIIqAACV8lP3Wq/taznB2E8XAwpx+z3s0cCq7GZmfjU+Ln35y1ImY7//jQ3p5k27ok8zL8BTaKYEAECl6t2EqBp+2tdc2YAdCu2cP+unCqwX3kMJDawaopnZ0JD0d38ntbRsH4tMxv77Aw/QzAuoM5opAQBQCzMz9sl/rlou61INP+1rrtxKoLR9Ozrqn6DqhfdQQgOrhmhmNjMjtbVJ6+tSc7O9ralJWl2lmRfgMQz9BQB4nmfXd4zF7JP9XF7tXuunfc1V7zVea8EL76EeDazcHt5cilhM6u62j8Hmpn27sWFXVGnmBXgKQRUA4GnZ9R2T6aT6In1KppM6d/GcN8Kqn7rX+mlfc/k1YOfywnuodQMrr82BLmRkxK5uP/ig1Npqh9SmJuk3fsM/FXqgQRBUAQCelru+Y5NpUnd7t6IdUU1MT7i9a/7qXuunfc3lVsB2sjrolYsE9zewkpx7j3s0a/KM7O/BwIA9J/Uf/2Ppm9+UfvM33d4zAPehmRIAwNPOnD+jvkifmsz2tdWMlVE8EdezQ8+6uGeom/FxO/DMztpVyJGR2gbsWjQ/qvd7KGV/nHyPJTRrAoD7FWumRFAFAHja2IUxJdNJdbd3b21bWl1SOBTWqadOubhnCKy9OiRnQ+fMjB3Q3A6dlXC6C7Rfu0oDcFWxoMrQXwCApzXM+o7wjmLNj/wyF3MvTjd48srwZtSGHxplIXAIqgAATxs4OKCTT55UOBRWPBFXOBTWySdPBn8ZDbinWPMjv8zF3IvTDZ78OgcaewvKxRn4DkN/AQBA6YIw7HUvxeZvfuEL/pmLWeyzqsU8XAQTw7pRQwz9BQAA1at3ZcWt4YbFqoNeWGomq9jx2euzogKKUnlhHWA0JCqqAACgNPWsrHi14ueV/dprP6iCwSl8l1BDVFQBAED16llZ8epcUK9UIvc6PlTB4BQaZcElBFUAAFCaeg579XLQGh62K0nT0/atGxXevY6Pl4Yow9+8cnEGDYegCgAASlPPygpBq7i9jk8tPyuWKmk8Xrg4g4ZDUAUAAKWpZ2WlHqHYb4Erd3/n56WlpcLHp1afVW6TpuZm6eWXpZ/9WemJJ7x//AD4Cs2UAACAN2WXV5mdtSuFTi6F45WmSKXKt7+Li1JPj5RMOn98Csk21tnYkK5e3V6mp7lZOnzYu8cPgCcVa6ZEUAUAANXx49qqfutk6pX9jcXs5W6mpuxg39xsb9/YkI4f9+7xA+BJdP0FAAC1ca/SN5Wc1diTmzpz6JLGfv+zmvrm/+72nhXn5WZN+Xhlf7NzY9fWpKZ7p5GZjNTW5u3jF1R+G74OlIGgCgAAKjc6qqmopXMfSinZsqm+jU4l243O/dWXNDU/5fbeFea3Zk1e2d/s3OGWFjugZv/09Hj7+N0vCAEvd75wNGrfnj7tz/cC5EFQBQAAlZuZ0cSxDUXTzereaFGTjLpNh6JzdzUxPeH23hVWZbOmqfkpjV0Y05nzZzR2Yaz2odwra1lmmzQ98oi0uWlXVY8ds4OrX9bWDErA8+paw4BDHAmqxpifNsa8ZYy5Yoz5Yp6f/ytjzC1jzKv3/vyKE68LAAAcVEmVKRZTvCWlyEbz9rZ0WpH9D+h68nrt9rVae3XFLXIspuandO7iOSXTSfVF+pRMJ3Xu4rnahlWnu/hWU1EcHpZee016/nnp4x+3K6p+Wlvz/oC3sSG9/7708z/vr+qqV4aDAzVSdVA1xjRL+kNJw5Iek/QvjTGP5bnr/2lZ1kfv/fH4xBUAABpMpVWmkREdTRolrFX77+m0tLGhxNBPqDfcW/v9vl85AazQ2pD3H4upKTvE9HFij6oAACAASURBVPRIQ0OaeOH3FO2Iqru9W02mSd3t3Yp2ROtXQa62EaZTFUW/rq2ZG/Du3LG7F29u2n/8VF31ynBwoEacqKj+mKQrlmVNW5aVlvRnkn7OgecFAGcEYS4SHFX3YZt+UOkwwuFhnfhvvqyFaLuW0kll9ke09Av/pRY+8KBOHD9Rn33PciqA5R6LREK6edOuGqZS0o0bir/wdUWm3tvxkEhbpLYVZCeHqzb6kNHcgDc3Zx8DY6T2dn8dC68MBwdqxImgelRS7r/W1+5tu98/M8ZcMsb8X8aYY/meyBjzeWPMpDFm8tatWw7sGoCGF5S5SHCMK8M2/aCKYYQDP/8rOvlvX1L4t/6N4if/ucKPP6WTT57UwMGB2uxrIU4FsNxjMTdnz8NsbrZDQFeXjq61KXF+578hibVEbSvITobLRh8ymhvw1tbsbdmGUJJ/joXTw8EBj2lx4DlMnm33j0l5QdI3LMtaM8b8d5KekzS060GW9VVJX5XsdVQd2DcAjS735E7avh0d5X/mDWpiemJr2KakrduJ6Yn6BysvicV2r9NZxjDCgYMD7h+/mRn7glSuSkJH7rFYW7MbBW1u2kuwSDqROKhz7dek1SVF2iJKrCW0sLKgZx59xpn3kY9T702q+rP2vWzAGx21j19Tk9TbK+3fb//cT8dieJj/lyGwnKioXpOUWyHtk7Rj7ItlWfOWZd27ZKV/L+lHHXhdANhbo1cOsEs8GVekLbJjW82HbfpBEIYROjVnL/dYhEJ2sx3L2qq4DdzK6GTqUYVDYcUTcYVD4dpXkKt9b7lTIObn7Qqcnz/ramXn137zm9Lhw/bFiEY9FoBHORFUL0gaMMbEjDEhSf9C0vO5dzDGHMn5689KesOB1wWAvdFsAvc5Gj6qxFpix7aaD9v0gyAMI3QqbOcei64uu+L24INSJLL1nAP/w/+qU0+d0rNDz+rUU6dqX02u5r3dPwUinbaHD4dC/v2snRKE7z0QUMaqtnOcJGPMz0j6PUnNkr5mWda/McZ8SdKkZVnPG2O+LDugbkhakHTKsqw3iz3n4OCgNTk5WfW+AWhw2RO0UMiupKZS9kkaJyINKztHNdoR3TFs05U5lXDe+Pj2kM7+fjvIVfu7XovnrOd+DA3tHuq7vGyHsvPna7W3ALAnY8wrlmUN5v2ZE0G1FgiqABzjlZNMeMbU/JQmpid0PXldveFenTh+wnshNfu9nZmxRwbwvXWfXz+TWMyupJqctiKWZVcQp6fd2y8ADY+gCgCAnzg9EsCvActL/Dw6g4oqAI8iqAIA4LJsFTeejOto+GjxKq6TwcLPActL/Bz2+A4A8KhiQdWJZkoAAKCIstdudbJbtZPrbzYyP3cQp2EQAB9yYh1VAABQRNlrtzq5zqWT628WUVbF2I/8vvYo620C8BkqqgCAXabmpzR2YUxnzp/R2IWxwpW/rNw1GoeG7L9jS9lrtzq5pmkdlmgqu2LsR0FYZxYAfISgCjSwssMIGkLZoeP+NRpv3LD/TljdUvbarU4O1axDwMqtGDeZJnW3dyvaEdXE9IRjr+E6hs8CQF0RVIEG1RAVEFRkz9Bxf/X0i19kDuQeThw/oYWVBS2tLiljZbS0uqSFlQWdOH6i8IOGh+0mPdPT9m2lgagOAavsirFfOfWZAAD2RFAFGlRDVEBQkaKhI1/19PXXpfX1nU/ilyYzdTJwcEAnnzypcCiseCKucCisk0+erN8czhoHrLIrxnCXX4fq+3W/AVSEoAo0qIapgARcLYZvFw0dhTrI3rhh3/HOHentt6VLl+zqHSeSWwYODujUU6f07NCzOvXUqfJDqodP0iuqGMMdxYbqe+E7VmgfmGIANByCKtCgqID4X62GbxcNHfmW6DhyxJ7zODcnvfuutLYmNTVJ+/ZxIukUj5+ku14xRukKXWz64hfd/44V+56zzBLQcIxlWW7vQ16Dg4PW5OSk27sBBFY25EQ7ooq0RZRYS2hhZYGTSx8ZuzCmZDq5tdSJJC2tLikcCuvUU6eqeu7sUiPXk9fVG+7dXmpkaGj3Eh3Ly/YJ49Wr9n+3t0s9PdL+/fbfjxyxh5uicoWOO8cW5YrF7BBozPY2y7KH8Pf3u/sdK/Y9zy6zdP9+Ly7aQ9oB+JIx5hXLsgbz/Yx1VIEGla2ATExPKJ6Iqzfcq2cefYaQ6iPxZFx9kb4d2yJtEcUT8aqfe+DgQP7vwsiIXeGQ7MpqKmVXU3/nd6QvfEE6fnzniSRzVZ1Rp7VQgybwa7tWotB6sNLu0RL1/o4V+577fR1bAGVj6C/QwKqeMwdXuTJ8u1gH2Tqs1+kKL8zbC+qxraG6djb3wnekVIWWK/rgB93/jhX7nrOOLdBwCKoA4FOuNbAp1EHWqyeS1YQIr8wN9cKxLdbkxoMhrW6dzb3yHSlVoYtNX/mK+9+xYt9z1rEFGg5zVAHAxwrOJXVLtunJ7Ox2FcTNE8lsiAiFdg5VLvUE10tzQ908toWO4y/9kvTcc/mPr2Tv78yMHWLr/F04c/6M+iJ9ajLb1+QzVkbxRFzPDj3r3At56TtSLS/8/nphHwDUTbE5qgRVAEBwVRsiCjWeabQGLoWO4/vvS4cP52+udfdu5RcIHFDLZmM78B0BgIoVC6oM/QUABFe+5XTKaRDD3FBboeN4927+7W+/7fpSInmHxv9wUid+64+dHabMdwQAaoKgCgAIrmpDhBfmhnpBoeO4b1/+7ZLrHWR3re069a5Ojv29BmYSzs4l5TsCADVBUAUABFe1IYIGLrZCx/HXfs27HWR1X2fzP3lDA2v7nK/y8h0BgJpgjioQYKwh6HHZpiEuNZtpGDRncUah45hvu1RdE6taYC4pAHgOzZSABpRdQzDaEVWkLaLEWkILKws6+eRJwqoXVNuNFvA6r10gCFJ3XgAICJopAQ2obmsIojKjo643mwFqqtB6u25hLin8xqNrFAP1QlAFAiqejCvSFtmxLdIW0fXkdZf2aLep+SmNXRjTmfNnNHZhTFPzU27vUv1U240WCDqnT9KZSwo/yY66uXHD2eZfgI8QVIGAOho+qsRaYse2xFpCveFel/Zop+zQ5GQ6qb5In5LppM5dPOdaWK17aC6nGy1X1dFoqj1JL/Q747Uqb6Pj37bCGHUDEFSBoMq7huDKgk4cP+H2rkny1tBkV0JzqcMQuaqOQoJ8kl/NSTq/M/7A51Qco24AgioQVLvWEAyFPdVIyUtDkysJzVVXYEsdhshVdeQT9JP8ak7S+Z3xBz6n4qpdAxoIgBa3dwBA7QwcHPBMML1fdmhyd3v31ja3hibHk3H1Rfp2bIu0RRRPxPPeP7ejcl+kT4m1hM5dPLfjQkBJSwMND+899HBmxg4iubiqjtyTfGn7dnQ0GMNZY7HdHXpLPUnnd8Yf+JyKGxmxLz5JOzvD0/wLDYSKKgBXeGlocrnzefeqwDo6lJir6sgn6MMCq+nQy++MdxQbns7nVBzNvwCCKgB3eGlocrmhea9hy47Ovw3ikhpBnltZL0E/ya/mJD2IvzN+tNfwdD6nvdH8Cw2OoArANQMHB3TqqVN6duhZnXrqlGvDlMsNzXtVYB2dfxu0q+q5J6/NzdLLL0s/+7PSE094M7B6NVQ3wkl+pSfpQfud8au95qDyOQHYg7Esy+19yGtwcNCanJx0ezcAYJfcOaqRtogSawktrCxshduxC2NKppM75t8urS4pHArr1FOnXNxzDxgaskPqxoZ09ap9AivZofXwYW+dqGZDdSi0c46YV/ZxfNw+6Z+dtSupIyPe2C+/yB6/mRn7QgTHz1mxmF1Jzf6OS/ZFlcVF++IDAEgyxrxiWdZgvp9RUQWAMu1VgfXS/FvPyc6tnJuzT2Cbm+0/Gxve6/jp9a6kDAusXNC7JntB0IenA6g5KqoAdiipWy32lD2O15PX1Rvu5ThmZSuq09N2QDVGymSk1lZpYMBb1RYqQv5XqGqa/R7mdhVeXraHn54/797+BkmtRyRQEQcCoVhFlaAKYMteQ1qBqmVPXt9/X9rc3A6qDz0ktbR4KygQZvytWFD6whe4CFEPtRqe7vVh+QBKxtBfACVxtFstkE+2gcojj9hBtalJOnbMDqleawbk54ZFXm0CVU/Fhm6XMiyVY1i9Wg1P9/qwfACOIKgC2OJot1qgkOFh6bXXpOeflz7+cbui6mDHz6n5KY1dGNOZ82c0dmGssvVrs/vpx66kzL+0FVtrdq+LEBxDbwvKOsJcDAGKYugvgC10q/UG5glXbtfw9df+Py38pwmdvLCpgUMfaox5bAxZtu11HIoNS+UYOs/JOaVB+HwYvgxIYugvgBLRrdZ92aCVTCfVF+lTMp3UuYvnKq8KNpgdw9f/4XV1/8cXFF1Y1cQjzcGoipVSgQlKtalae1VNiw1L5Rg6y+kKtZ+H5WcxfBnYE0EVwJa9ll1B7TFPuDo7hq+/+KLU0qKIadf19nX/nwiWerLfCMuClBLYqxm67fYxrHZIqNeGlDodyvw6LD8XF0OAPbW4vQMAvGXg4ADB1EXxZFx9V+9I3/madPu2dOiQIp/6lOIPde/9YOho+KgSawl7+Prt21JXlxItG+pdC9l3yHciWItlLmrxnLkn+9L27ejozuceGbEDrLRzSGGNq011G7KeO2QyN7DnCyrDw5Udd5eOoaTy3l8tHl8LMzP2vuQqFMpK/d2p9LP1ilhs9/DloF1QAqpERRVAoDnWWKdOjl5dVOIv/lS6c8c+gblzR4m/+FP1Xl1we9d8Ycfw9UMHtWStaCG0qRO39tl3yNfZ1emmObVqxFNqBcaFalNdh6zXY8ikmxW7at+fF4eUllqhbqQmVkEYvgzUGEEVqCO/hSa/8+N8zxN/PqmFTqOlziZlZGmps0kLnUYn/pzmcqXYMXz9pz6m8Kqlk291amC5Pf+JYC1O6msVFMoZjlqrZUEKqOuQ9fsD+5070rVr0ve+5+ww1/uPoVSf4bTVDgn14pDSUkOZF0N2rQRh+DJQYwRVoE78GJr8zo/zPQfeuKmTNw4rvNmiePu6wpstOnnjsAbevOX2rvnGwMEBnXrqlJ79/Dd06lf/RAPh/sIngrU4qa9VUPBwBaauS1vlBvY7d6SrV+3j0N5euwpcPSt91c6PdXt+bT6lhjIvhuxaqvMFJcBvCKpAnfgxNPmdJ9eF3avJSSymgVsZnXr3AT379lGdevcBDdzKBH/eUq2av+x1IliLk/r7n/POHenNN+1wU01jnC98Qdq3z64weawCk50bnCuxllBvuNf5F8sN7O+/v7398OHaVeDqWemr9oKEVy9olBLKvBiyAbiGoArUiSdDU8DV9eS5FKVUZbx6kllLdZ6XtmMI/mc/rKm2u84e79zPcGnJrgatr0tHj5b/3u4/Num0dPeu9Id/6KkKTN2Xttq3zz6ud+9KTU3SQw9J+/fbP7u/AufERZBSKn1OXWypdkion4eUNuK/fwAKIqgCdeK50NQAPLcubClVGT+fZFaqjtWqXUPwBz6gc6ee1lQs4tzxzv0M43H7vXzgA1J3dzAa4+RRt6WtssE9nZYee8wOixsbO++TW4Er5SJIKQFzr0rf+Lj0y78svfyy/Zm//LL99/ufq9QwW+2QUL8OKW3Ef/8AFGQsy3J7H/IaHBy0JidpHoLgyJ4gRzuiirRFlFhLaGFlgXVKayy7ZMb15HX1hntrt2RGKWIx+2TZmO1tlmWfkE1Pu7NPXlDH4zJ2YUzJdNJevuaepdUlhUNhnXrqlKOvJan699YI35lylvIZGtq5pMedO9K770qtrdKjj24vI5MNN7n3v3NHmpuTVlbsiuzXv24/R3Ypl9xlaO4PR7lLvuS73xNPSK+/LrW02BXeTMYO0I89Jr32WmnPAQANyBjzimVZg/l+RkUVqJO6VRyww1ZjnaFndeqpU+4eb+Zf5VfH41L3IfhBbIyT5cRQ13KHfd8/BHf/fnvYbyaTvwKXvX+26dL6uh1ql5ft1/niF0urWO9V6Xv7bam52Q6pkn3b3Gxvz/JJdTzQajUXHkBNEFSBOvJUaEL9Mf8qv3zHZWlJmp93/ISy5kPw7z8R/sQn/NkYZ68TeqfmFZcb3vIF99ZW6emn8w9zzd5/bs4Oj9lqZ3u7/Tpvv116l9lqh9M2Wkdbr2mkNVqBgCCowlWsK4qGwvyr/O4/LqGQHcrSacdPKGs6bznfifBzz0m/9Ev+aoxTygm9U9XBmRm7yvn229Lly/bw2dlZ6bvfzR+Qyw3u2fuvrNj7ublpP66nZzs0OlGx/uAH7QCcff7NTfvvH/zg9n28XB1vBFS0Ad9hjipcw5xN1Et2nmo8GdfR8FF356k2kIqP+/3zECU7kBw5YleyKpEzD3Lqww9q4p8P6vpDUWfnLRfb75GR0udhuq2U4+/U3NncuZ2WJa2t2dvb2+0AV2i+6Be/uD2s9oMflL7ylcLHc3xc+sxn7PfQ3m6H1P377b+HQnbn4GrnjY6PS5/7nJRM2nNTW1qkcFj6D/9h+3mYo+quRpjvDfgQc1ThSawrinrY1eU1ndS5i+eo3tfS+LimfubjOvf5QSX/4N+p7+qd8o6700Mk76sQDswkdOrZ/1fPrv24s0PwC+3366/7a8hhKcffyeqgMdsV9FzFKl5379qv9dhj9uOKHc/hYbtx0rFj9hJBkch2JfYrX3GmYj08bIfSj3/cfo2Pf3xnSM3ehxEV7qGiDfgOQRWuYV1R1EO1F0QYnl6me6FwouldRZv3qXthWU1/9mfqvnKt9OPu9AllvYb8FdrvtbXavn61DWLuf3wksvfxd2rubCJhN0PKDvduarLnnGYy9s/zXaCo5PMsFhKdWsqllOfx67IxQUCPAMB3CKpwDeuKotIT7HLCYzUXRLxejS16HNzqbnkvRMT3G0U2mu0A0dIivfhi6ReinD6hrFcTm0L73dZWu9evtkFMvsfPzdnNrIodf6eqg7GYHUw/+EF7qGwoZIfVtjb75/kuUFT6eRISG5sXK9p0IQaKIqjCNTVtahIwgazqVXiCXW54rOaCiJeHpxc9Dm52t7wXIo6uhpRo2bS3hULS7dulX4hy+oSyXkP+Cu33Y4/V7vWrrRbne/yBA9KDD+59/J0Ifrnh/sEH7fmdmYz934UuUDCEE5Xy0sUKuhADeyKowjWsK1oat6p6NQ/HFZ5glxseq7kg4uXh6UWPg5vdLe+FiBO39mkhtKmllg1l0mta6uku70KUkyeU9Rzyl2+/a/n61VaLCz0+mazPCX1uuM9k7FD/4Q/b/10oILs9hDPoVbCgvz+voAsxsKcWt3cAjW3g4ADBNEe+Lqm5gUTS1u3E9ETRY1dNp9vcjsx9kT4l1hI6d/FcSRcSSn7dmRn7KnKuEk6w48m4+iJ9O7ZF2iKKJ+J575+9IDIxPaF4Iq7ecK+eefSZko5FthqbPeaSd4anFz0OFR5bR4yMSKdPa+BWSCd1UBORecXb1tQ79M/0TJ7vT106Mg8P20vE/O7v2k149u2Tfu3X6ldNyYax0VH7M+jvd67rbyy2u0NvOdXFah9fTE6n5aKdjrPzREtV5vF09DuW27k3twrm9hBSpwT9/XmJm/9OAz5BRRXwiEKV08tzl8uu6lVbha10yGtZr1vh8L1KhvIOHBzQqadO6dmhZ7fCfymV4pKrsS5UIIoeBzeHRuZUyAaurehU6jE9+ytf16l/9Yd5Q2pdRguMj9vrmR4+LD3+uH373HP1rRTVashhtdXFWlUnaz2sscTj6fh3LOhVsKC/Py9hCDuwJ4IqShKEOZLlvAc33m+hcLi4ulhyMMvu96+O/6remn9LaxtrFc2trHTIa1kBt8IT5GqG8pZ70lrS8PQanZDv9R0sehzcHhpZYogo+n1xMvwH+eS72vm8tWow45Fj7vg883o15nKLE++PocOlcfvfacAHCKrYk9c7n5ainPfg1vstFA4PtB8oKZjl7nfW99/7vubuzm09V6lzKyttQFRWwK3wBLmauc2VnLTmVmPzrrlZgxPyUr6DRY+DF7tb5lHw+3Lxu1WF/10h//ZbwQ4X1VZra1HtrUegKyEQOT7PPOhVsGrfHw2CSueTf6cBNzFHFXuqdI6kl5TzHtx6v4XmQ36k5yNbw1WLzbHM3e9oR1SrG6vqbO3Um7ffVM++nrLmVp44fkLnLp6TZJ/UJdYSWlhZ0DOPPlPRe2g2zRq7MLZ7jljO3LSteWTnz+w5j6zSuc3lzm8tSQ3mGZX6HSx6HMqd9+eCgnOA//YH2+Ff2r4dHd3zPeWdX/1Us05eWtSAlfM5lRsuSp1vCVst575KJc+ldHye+b052JLs3/NUKlhVsE98Qvryl6XNTXuJoO5u+xiX+v5yL9xJZf3uNiQf/DsNuImg6hG1aijixPNWe3Jfl2YpeyjnPZRy31q8p2LhsJRglt3vubtzSqwl9Pqt1xVuC2t/2/6tKuxeQTOr0gZE+d7DlfkrMsaoM9RZsDFTNc2bylGT5khFTsgr/Z7UJFB7UMHv/OW7UufhnXcuMfznDfk/fkIT11/QwGxbZeGCBjPlq3WgKzEQVXrRraBaNsZyW3Yu96FD9jq6q6vS7dvSb/xG6e+PBkEAHGQsy3J7H/IaHBy0Jicn3d6Nusg9SV9ZX9Grc69q7u6c/snD/0Sf+chnKj5Rzz7vZmZT15LXdPPuTbU0tejXn/51feqRT5X8PGMXxpRMJ3ec3C+tLikcCuvUU6eKPvY7V76j3/7739Z6Zl2Huw7raOSompua674MTTnvYa/75n5euSc+5bynQgEmu/168rp6w71lBeCxC2OaXpzW5ZuX1dXapfRmWjOLM0ptpPTZxz+rX3z8F7efq4bVofvfw83lm+po7Sh67Isd861qcoXdi3Mf+8iBR/TS7EtVfXa75IaYnBPyqdHf0LnIlYpeq6LfOZ9W/PJ+53/hv90d/peX7aFx588Xfb4z58+oL9KnJrM9syVjZRT/wd/q2T+bqyxcDA1VvD9BVPIFmOx3shaBLhazA5Ex29ssyx5COT2dd38r+Xe1oTjxPed3BUCZjDGvWJY1mPdnBFX3ZU9K1zbW9P33vq/O1k7JkixZ+tChD1V8Ep0bXDpbO9XR0qHFlUVtWps6+zNnK1qqpJwT7qn5KZ3+1mk1m2Yd6Dyg1fVVLa8v6yMPfkTHDxzfccK914lPpZWp7OMuz13WO4vv6PGex/Vw9OGi7yEbrjcyG3pw34PqC/ftCNfVBPdqj+dex2jreHcc0MrGilLrqd3Hu0CwqlV1qGBwSMT17NCzRe9z6f1LirRHKgp7hY7zJ/s/qSuLV5w9ac1zQj52aLbi70k535Gp+SlNvPB7ir/wdR1da9OJxEEN3MrU9DOtuSq+o9X+fuZVRigKOicu1Dmi0QJRsQtRTl2kcuJ7Xuf/vwDwv2JBlaG/HpAd5ve997+nztZOdbZ2ypKlpZWlrUYv5Z4ATM1P6fm3ntfVxFW1NrXqochDMq1GBzoP6P3k+2U9Z6XDQCemJ7SR2dChfYdkZNTR2mG/30Rc7S3tO/a12LDP+3/+zsI7Ov2t03r4wMP6SM9H9MiBR3Rl8Ure6mT2cY8fflxdoS5dmruk5fSyPtLzkbzvYWp+Si/NvqTHex5XPBHX3N05LaQW9OtP//rWfe8fljl3d05v3HpDVxNXJWlXdTS3mndl8Yqef+t5tbe262OHP6am9qaS5sCWMjR24OCAjh84rqW1JS2tLqm7vVtPHn5SD3Q9sHPIaJ3nEJUy3LbQfRZXF9V/oL+i+cKF5nleWbxSeWApJM88o/i98C1J+uEPpRdfVOT2LcWPhqXb/UWPdam/c1PzU/rt//Tbujn5F1o9lNY/SHr1gTn9uno0cCvk+Gdat2H8VQyvdHyop1T7+ZY+4pmeBcWGFvt0dEFBxYaeS84NS3fiex7kodEA6o6g6qB8J3GStrYlVhN6Z/Ed3Vm7o2ORY/rlj/6yPvXIp9Ta1KpvX/m2Ls9dVrQjqiPhI2ppalF3e3dFc0EvzV3S9OK0MlZGG5sbCjWF9M7iOzrcdVgLawtKb6T1wlsvFDzJzK1CLq4uqru9W4/3PF72SemluUu6s3ZH8URc+9v36/C+w9rXtk/vJ9/XT37gJ7fut9eJT/bnaxtr+qvZv9Lrt19XR2uHWpta1RXq0nOvPaeffOgntyql2QB3//MOHBzQA10PaGV9RZL03GvP7TrZzn1MdtvS6pKuLF7Rp2QPl84NVXN35/T9976vJjXp2P5jW91ZP9n/ya0hpn2RPl2IX9C//f6/1eGuw0qk7ZD2/fe+r5849hPq2dez5+dc6snh4z2P560m7ZiDWec5RKUEh0L3OdB+IG/HzlJ+J9ye57n1PblyTfrGN6SWFiX2t6v35mpJJ5KlzEv++uWv6635t3RweUXR5jatNmf0Vteqvt67oN9a7nX0M63XPOItFTYZqfTCWlFBb6BThl2/Vz/8oSIvflvx1Jx05z/WL5QUCkRS8OYTF7u4KDl34dGp73nQGwQF7UII4GEE1SpMzU/pD17+A/3lm3+pueU5ZayMukJderLnSR2PHtfZl8/qyvwVbWhDkmRk1N7SrnBbWJfmLukv3/pLPdj+oDa1qZamFm1qU2/Ov6nX5l5TqDmkWHdMqfWUPnr4oyUN+8yeRC6tLanZNGtlY0WbmU2tZ9aVsTK6fOuyutu79YH9H1Bba1vek8zcea3vLL6jZtOsxdVF7Qvty9sA5+uXv66XZl7SfGpe0Y6oPhn7pH7x8V+UJE0vTquzpVOp9ZSW15c1tTClQ52H1NHSsWNplUKB4vLcZY1dGNOfXv5T7W/br8RaQsl00g4uRnrj9hsyxijaHtX15HUNHBzYEeDyPe/K+opefOdF/dyjP5f3ZLuUcJMbqt649Yaa1KSMMnrs0GNbr/+1V7+mJ488uRVm//7a3yvUHNKmtalQc0jvM3hUcQAAIABJREFU3nlXse5YyR15Sw1dJVWT6lwdKiU4FLrPxPRExc2PatI4qQxbn8VL31KkpVmJziYthDb1zPsHpVDGkWrnSzMvKbGa0O1DUtdqSofX2xVdb9bL0ZTjn6lnKmklKKsrdCknnVSJtuz4vfrhD6VvfEOJDqNeE6l/KMwXiIaGgtd1ttjFRcty7sIj3/O90VgNqCuCaoWm5qd05vwZvTT9khJrCW1am7JkKbGW0Hevflffu/o9bWpz1+OWN5a1srGituY2rW+u69rda+oL9ym1kdJ8an7rfplMRtOL05pfmdebt97Ud975jp4+9vSuqmG+SmBiNaEDHQfU0dqhFtOiueU5zafmZYxRrDsmY4w+dvhjamtp23GSOTU/pd/869/U3PKckmtJHeg4oGhnVKn1lK4nr+uJw09s3T875PDV91/VwsqC2prbNLc8p29NfUuv33pdy+vLSqwldGftjtqa27SwsqDF1UVdS1zTTz30U1vrVg4cHMgbKN5ZeEdXFq7oA90f0EORh/Ta3Gtaz6xLsk+QNzIbirRFNLM0o8d7HtfS6tLWY7MBLt/zvvr+q3pw34MFT7ZLCTe5oepq4qqO7T+mxw49pp59PVuv/17iPf1U/09Jkt68/aYyVkb72/drZX1Fxw8c1xu339Dc8pyam5pL6shbaugqFPgkbS8P89kP68TvTWlgWXWrDpUSHArdp9JhnPeH9ncW3tGluUt6+MDDGrswVvOGKlufxW//e8WjIfWuteiZq90aSHVInVbV1c6p+SldS1xTZ0unuh7s0/rVWV3pWFHvWkgtGxnHP1O3K9Q1Uc5JZ9CrRCXa8Xv14reV6DBa6GrSM1fDUpc9vcORUFhp1SqIXWf3urjo5IVHvufFsfwOUFcE1QpNTE/ozdtvaj2zrubmZlmblizL0qbswFpMRhmtZ9bV0tyi9GZaS2tLWttc2/p5a3OrNjObWsusaXV9VZFQRO8svqPrd6/rRw79iH6s78d2zV2NJ+NqbWrVa++/ptmlWYWaQ4q2R3UzdVOHOg/pduq2Im0RHeo8pEcPPaqefT1bDW2k7UrqzeWbOrzvsK4nryu1kVJHS4f2hfZpaXVp66Q0G2h/cOMHSq2nFA6F1dXWpdbNViXWEnp17lUZGf1o74/q6tJVvTn/ptqb2xVqsiuKF9+/qCuLV/TVV76qf/roP9WP9/24Xpp9SdJ2GLk0d0mP9zyu7vZuffiBD+vl+Mtqb2nX3fRdrayvKKOMjncf18zSjJZW7Ary387+rZZWlxRqCelY+JiaTbNenH5RPV09+ujhj6qjtUNzy3Y35Vy5J9ulzm/LDVX3D7VNrCV0LHJsK1hmG7msrK+os7VT4bawPnTwQ3p7/m1ZlqVwKFx0aOLU/JRuLt/Ut9/5tnr29eijPfZ7KRTY7g98u4ZsDkR07tTTOvl/v6uBN2/V9ap5uXMcqxnGmfvYS+9f2tVMq6bDVnP2YSD9hHTR2eVrJPvfoOMHjuvqnatqjXSp9aF+rc9d07ttK/pMqs/xK/xuV6hrotFPOisIgzt+J1Nz6jURPXM1bF+AkZwJhdVUrWoxYsTtoZ57DcllWHr9BPFCCOBhdP2t0JnzZ/SNy9/Q3N05bVqb2rQ2tZHZ2DOkZjWpSU2mSZZlyRijjJWRkdl6vCVLTWqSJUstTS1qa2nT0fBRtbe0q2dfj57ue1obmY2trqn/+m/+tf569q91sOOg0ptpXb55WfOpeXW2dqq7vVu3U7cVagrp2P5jW6+R3kwr1BzSI9FHdC15TYc6Dmkjs6HW5lbFE3Etry+rq7VLRyNH1dHSoScOP6FUOqXVzVX9zczf6N077yqxllBLU4t69vWou71b7yXeUyQUUWtzqz508EOaWpjS7dRtJVeTsoyljuYONTU1qdk0qyvUpf7ufv1o74/u6sR6ae6Snjj8xFYX2BfeekHv3XlPiyuL6mrr0rHIMXW1dimZTurm8k21t7SrN9IrYxnFk3Gtbqzq0w9/WvtC+/Tq3Ku6efemPv3wp2VZljpDnbvmcKbSKfXs61E8GVeLaZExRhuZjT27whbrLJudo3rx/YuK34nrWvKaBqIDOtR5qOTuy7uWLnr/Vc0tz+nTxz+9c7mZImrSBbUCbnYLdfUY1GD5Gsn+N6i1qVUT0xNKppPayGyoualZTWrSn//CnzvXyfjeyfnU6c9Utb+e1MjdfJ3ozupU590vfUn63d+VkkkpHJa6u6X29sqe1+mus17pYltsqZ9aLgOEnRqt2zRQB3T9rYGj4aPaF9qn2023tZnZVLOata71kh9vydKGtZH9S17ZocPrmXVlNjJ67857CjWHtLKxopnFGfXu69Xw/zGs5fVlXb9zXcsbyzq877CORY6pSU3asDa0kdlQqDmk9pZ2JdeSurJgn2gurC7Y81jXV3Rl4YrWNtfU09WjaEd0K9wm15KaX5nXgY4DevjAw1pYWVBHS4c2M5tKppMyxmzNu5y7O6dm06xMJqOO1g4dixzTrdQtxRNxdbR0aC2zplCTve/htrCMMdrXtk837t5QtCO6qxPr2P/P3puHt3Hf576fAWawAwTABRQ3URQhUrJESd7ipY5tWZKrNLYSt05juU/cWm3Po9w0z2mOfXp6ruM4SXvT1r5tepscPbe3co9zaqe3dR3HTqJrmbZsJ7K8xdocmRRJkRJXcAFA7BgMMPePEUCQBElwk+UYb6KHJjjA/PCbH8jfO+/3+77vHpym3lxfez1hOcw61zq2VW/LZc3uXr+bidgE/aF+ZEXGaXJSZa0inUkzFB7i1sZbWWNfMy2Tc6Zi2j3RjSAIWAyWXN9qsRvw+UptzQNmjvYeJSJHUFWVa6qvoS/YR+dEJ2bJzH+98b/Oa2Y1GB6kx99DnaMOp8mJ0+Sc9l6KJQeD4UHqLk7Cy09q4e0VFTh27WKwwVnw+NVydv0oexw/0rLVOfq+2iv6cMtLn49aey1hOczu9bvpGO/IVRNsrdq6MiR1hqLlffg77H/8z2kvS02tdZ9byzz9KE1FlqN2fZLdfFdCTV4J851vfQu+/W3Q6bTxxGIQDEJ19fTrUqxqtdJ9lleK6j5fSW6pXPfyoWSsVkIJlxW6hQ8poRB2Nu2ktaIVSSeRTqdJZ6b6UYVL/5sPxSqv2WPTmTRJJUk0FSWZSjIYGqRjooNQIsRIeISJxASiIJJQEvxq7FeEU2HWOtZSY6/BIlnw2DxYJAsJJcFYbIyEkiCYCJJKp0goiRyx80V8iIJIhaWCMlMZTqOTJmcTTa4m9m/fTyqT4sOxD8moGRJKAjkto6Q1QjwRn8AkmjDoDVxfez1lxjLMkplkOplTUHU6HXEljkFvyBF0h9HBUHho1vz6436CiSAZNYNRNLLBvYFt1dtQMgq7m3bzPz/3P/nGbd/AYXJwZ/Od7G3dy62Nt5JW0zjNzll9q1nDpf3b92M32BkMDWI32Kl11LLevR6nyYlO0OJisqXVxcBb7uXAdQf49o5v58j2oROHMEtm9rbu5a6Wu6h11DIcGcZhcnBj/Y181vtZOv2ddE10TXutrOoYlsPUOerwRX2cGT2DL+Kb9V6KRe3FAKH/eBomJ7VN1uQkof94mpqL/lnHzjx/1sV45jiXgtO+05wYOcGPO37M632v44v4Cr+Xw4e1u9br1mlfDx9e9rmzZav5uKxlq3v2aHfbz5/Xvu7Zw2B4sKCjcbHXNvsZMYpGbll7C7c23kpLeUvOzGxZyN+cC4L21WDA+71nptb6eCPeh7+jEb388swVuF5FI0uolzqGhx/WNpk+H3R2wqlTGrm57bbVHPXKYymfmd5ebaOdj8WWMGZJ4Zo1mgq9Zs3ilca/+zuNpIqittayX0dHpx+3mBsIBT5vS8ZKzFMJvz5YiTVfQgklFI2SorpEeMu9/MWOv8Bj9fDjjh/ji/owCAYAqm3VVFuq+WDsA2Lp2LTnGQWj1qOqppB0EkpGQUBAJ+hyhkxzQa/XI6gCgWQAk2giraZ5f+R9JL1ERs0QTARJk8Zj8xBOhhF1ImbJrJXwGqzIigxoxDeTyaCoCjp06DN6zJKZiBxhNDZKSA7hT2gxNk3uptz5+wJ9nBg+wau9r2plzpfKllVVRSfoSKQSbKzYyBrbGoyiESWjsLFiIz2BHuwGO5FUBAEBOS3nxtVS3jKNMOSreSa9iXgqTjgZpsZew8M3P1xQKZoZFzMWHePc+DlcZleO4L0/8j6JVCJnopOv3j7y6iN4jJ5pr7kcta2QciinZSptlexpnvpjFkwEZ6ln+VE8Px/5OaPRUVDhHd073NVyF7B4grXz397jkF0AVYdDUQlZdPQY06x57ac80uCappquhuqZdYd+qeclzKKZJmcTcSXOsf5jbKnaQpNrao2tlqPiqmRrLhPL7flclRiWLIrpw7oSlKbljmHPHnjgAfjOdyCd1spNnU546im6NtfQXp9a/czY5WKhjM251OaVUpMLqHmzqjL6Jbzfe6bwOMJhbez5kCTtBkI0unzVqhjFfb5jPsmqewmFUVKwSyjhsqHUo7rCeLn7ZZ48+SQDoQHN/TXQz0BkAEEQMOqNmCQTI5ERJJ1ES0ULfYE+zUhJ0KJTss62QK5HFTRyaZfsqKiagRN6dDodclrGKBqJyRohdpqd1Dnq6A30YpEsbKnaovVgKjF6A71k1AwmyURcjpPMaAZOok5E0kkklSQqKgICdfY60qRpcjVpJlGZNBdCF0jICfxJPzp0OdU4Q4ZyUzk7m3eyvXo73RPd1DpqeXvgbYySkQZHA+f95znpO0k8FSetpqm11+Iyu7ih7gb0Oj37t+8HmNXD2OPvwaAz0B3oBuCGuhvYt2XfnJE6Z0bPEEvF6J/sp76snoyaQVZkLAYLtzfenjMhyp6v/Xw7L3S+gEkycXX11Tnn3uX0Lz7y6iPUOepy/bUAP/rwRwiCwOdaP5d7LGtm9e0d385t7J4+8zROo5PJ5CRV1irktEzHeAdyRubBbQ9OG3++W3N2UyjpJFRVKyvPba6v3U1XvYX2yghDRhkxA4MmmeZxFcdj/8e0UuenTj01a+z541wsstemc7yTiBzhwuQFBAQ2lG8gnUnP7tNdTP/PIks+s/OU7YNeCeKxnDLpj7Jnd0EUcx2uhP7OlRhDgffaJfg51KbgfvArV961mYm5rpXBAJHI3L2VS+29XOBzN2tdn3oH/+Hn2H/BjTfj0pTS8XEoK4NNm+Dtt0FRNCU1C0XRyOr11y+vfLeY97jQMVdKj2oJJZRQwq8p5utRLRHVVUbXRBePH3ucsfgYSSWJUTTy7sC72I12TJKJYDxIXImTVJJEkpFc5qoePRkywCXCKqgY9AbKDGXElBhJJYmoE0EAVdUchzNqBpNowiSaUDIKFoOFFncLoUSIvlAfwUQQAQFRp5UIZ18/v1Q5QwZJkCgzl1FmLEMvaPEpcSWOVbIyFh0jQwa9oEfl0nnRjKA2VWyi3FKO2+Jma9VW7m+7P/feJ2IT9E/2E0wEMYkmbAYbTqOTCmtFjnxmTWGSSpKO8Q4GQgP0BfsAuLrmagRVwJ/w01LewkM3PTSLrD569NGca7GoEzk7dpaeQA9m0cy9m+5lU9UmQCOh8VQ8lzP74diHnBk9g1E08pvrf5O6srp5yeBChCTfuMcX8dEx3sF7Q+9hEk38zqbfmUWGs2pf1njpzMgZ5IzMxoqN2I12xqJjjERHqLXVclfLXdPOPdNs6WjfUYDppPxvj+LtDeU2sgfXjhHOxHFa3PC1r00bC8x2MV4Oac/OxWt9r+Eyu4gkI1ycvEgqnaLN04bL7OL7v/X9qevYVkt7s55Bc4rahIGdYza8UdNs4nEFbB5XgmiuBnleERQzv1eCqchKjKEA2T3YMEpYjuD85l/lHivmc7Ba/d3zYi6yfvasRvDmm5vFmvAUsS5mGZf97d8SjPmx68wcOG2Aixc19VpVtZJf0L6XJNDrtf/OZODrX4dHH13e3BSzPoo5pmRWVEIJJZSwapiPqJZ6VFcZ3nIvD9/8MLubdnPNmmvY3bSbezbeg91gJybHMIkmMpkMaTWNKIqUm8rRo59WApwmTaWpkmprNQ6TA5fJpZXQZmQsogUlrZBRM7mezzJTGTfW3UidrQ5/ws9wdBirZEUURE0dVdOY9KYcOVWZIpuiICLpJSYTkwyFhugL9uWIsF7QkyaNpJOQdJJmnnSJ7KqoDIYGeW/oPd7oe4NDJw7xiwu/QBAEwokw5/3nicgRnCat5zWZTqLT6bih7gbMkpkn3nyCZ848w4udL/LDD37IeGxcU5jTKSLJCOl0GovBQrm5nNHoKM+ceYaD7x7kkVcf4eC7BwFY717PvVfdS2tFK76oj4ayBtbY1mA32OmY6MiVATuMDt4eeDunwBpFI22eNgx6Az/t+inxVHwWSV1M32a2d7BrootfXPwFgXiASmslRr2RV3pfYTg8nMtO3dm0c1q57aaKTcSUGAa9geHwMLFUDEEQuKf1Hq6puYYD1x0oWCrsNDk5N3GOcnM5brObcxPnpnptv3DtVBmdqjKoj+KIZ2D37tzrZHsjZ/YG549zKcj2YbpMLhKpBHajnU2Vm1jrXMu2NdvYXLU5d2zXRBeHrtMTVhPUxSXCeoVDDRN06QKzy+wK9VDKMuzbt6K9rfMhf+6X0tsMs/ubrwiSCsX1YWX7Oy+tK6LRy28qshJjWLdOI1x5GBRjOMoqpz22UP/wavZ3z4sC4899P19vZb4yWiz5mqN3mccfzx0yq/d6fByHYGLIeKkXWFU1Iqqq2nN1Ou2fJEEqpY1xJUgqFNdfWswxK9nzWkIJJZRQQtEoEdXLgJmb0T/51J+wfc12zZ1X0GE1WDHqjTgMDppcTWyu2ozNYNOUR8mGzWDDYrCwzrmOlvIW7EY7DqMDm2RD0ksYRSOSTkLUi9iNdjZWbMQoGqmwVBCRI5SZyrip/ibsRjuiTsSit6DX63GZXOgFPaCpqtnXEQQBvaBHTssgQDQVRckoBJOaIqtkFC2SJ5OeZhoVVaK5WJd4Ks5f/PwvcBgdeGweKqwV1JXV4TK7GIxoGxk5LXNu4hxJJcmJ4ROcD57nw/EPGQwN8s7AO/RN9iFnZCS9xCnfKU6NnOJi6CIDkwMc6Tkya0Mo6bQc147xDqySFbNk1hyPJRNWyUrHeAeg9QFmiXX2uDJTGVs9W2l0NlJlrZoiDIcP0/7Vz+L+7j/i/B9PovvV2QUJSbZ3cCA0oEWGCNpcmyUzgUSAV86/gt1gz5Hh/I2dx+bhqoqrEAURf0JzWb65/mbMkrlg72L+cwOJACbJhFk054ykHEYHQw3uaaSjViondO/dsHmKJGZ7IwuZTS2n3DHbh9la0Uo0FSWeimtEXDTMIsDt59tx37QTZ1xFJ6dwKiLuaIb2ivBs4jFzczk5qW2Co9HLZuyzXDOkFcMqmE8BC2/OrwRTkZUYQwGyWxsWCO24edphC/UPF3XjYjWu1czx+3wayUqnoaND+2xkke2tXKoJVRGkbpZxWUUFITVBTdIAyaRW1gtTCrBerxHV66/XiGogsDIkFeYm8fk3voo5poQSSiihhI8EJTOljwDeci8P3fTQtJK/M74zBJIBkkqSgdAATe4mDHoDk4lJ7AY7lZZKXGYXtzbeii/i48VzLxJIBNhctZlIIkKnvxMlo7DNsw1RJxJNRTXSeYlICoJAmamMVEZz+RXQvrdKVibiExj0BjJqBoPeQCKdQCfoyJDBgIGkksy59FpEC1ElmsuM1aNHQUEURE1hVVIIcpzKiMCwNcBR+XmCBpWYEqPCXAECTMQmkNMyqqpSZiwjlAjlSHBU1sadJk1KTqGoCia9CVVQ8Uge4qk4voiPtuq2nOFQNpajzlaH1WhlODJMta2aWCqGzWADtJLmQDyQUwg/Vfsp3rj4Bmvsa3LXJa7EqbJVTRGNS5u5wWsmqTPYtA3fD38I992H46pN85otecu9rHevp7WileMDx7GIWvSNW3Hji/hodjXTfr6dp049RY+/h6SSzJHB6+uuJ9wbZp17HbesvWVO45+uiS56/D282f8ma2xr0At6EqkEKmqu7C63ud4xZf6w85LyQyJY0FjIW+6dVVZ98N2DSypnzC9rvrHuxnlzYAfDg9RtvR5EGxw5oikxFeUM7rl6NvGYaXDi82kbX6NxSumBVTX2Wa4Z0opglcynisaVYCqy3DEUiDPZ+Uf/hUOO7jk/I4WwYATSal2r/PGfPav9nqqo0D4LFy5o/xoapgyKHn546SZURRgLzTIu23ET/sPP8fkLNu2csqx9RiVJe0Imo411NVx0i4kSKcWNlFBCCSVcsSj1qF4hOPjuQc4HznNm9Ax9wT4cBgcxJYaqquxs2knnRCepdIp7r7p3lsmQgECzu5meQA8JJUG1rZrWilbeGniLwdAggk7gqsqr6JroIpwM0zfZh6STsBqslBnLNOfhPCVUTsvIaRlBFRB0Asm0RlSrbdVE5AjBeJC0mgYVDKKBVCalvYaqIqQyVMl67GmRi6Yka+J6XGuaGNCFkRUZQRBIZ9KYJTOomvlTKp3CKBqJyJGpCB4lmRuXqqo55VNFRc2obKrahFkyY5EsmEUzMSWGL+Ljm7d+kydPPklfsE/r05UsOAwOEukEkk7K9XgCfOVnX9HUTrOLuBInlorlXGgPXHcg17t0cFOMsF7BqYggy/jKTby/ZxsJJcHdLXfPSdwOvnuQI+ePIKC955HICJPJSVChwlLB3a134zA66PH38POLP+eWhltY715PKBni/aH3CcthJpOT1DvqeXDbg+xq3pV77Wzv8/ngec4HzmORLBj1RvQ6PRZptnHUzPEV2xt5OfswZ/W2XXruQGiA9e7100nyzF6506c1VaahQTNpgdU19jl8mK5/eIxD5g7c1kocO/YQ8tZffsOdK6FP9NcURa3bvPLZgzcZCO+4Gee2G3I/ntbXejmu1cxzTE7C0JBGBm+8caq8d6kmVEX2hs+au6zr79mzMDamnddgmCoBrqoCr3d11mwx/aWL6UFdTm5vCSWUUEIJs1AyU/oYIN+59mjfUYKJIEa9kV1Nu9hUtSm3YW92N8+5aZpJKl7qeYmh0BAGvSHnIHty5CT+uJ819jXYjVqfrM1gI5VOIeg0sooKbrOba9Zcw4vnXiSdSROSQwgIGPQGdq3bxVh8jBp7DUf7jhJJRphMTpKRZVQBbIpAWgf2lIBT1lOXttGz3sVwZBg1o2I1WAnLYfQ6PU6jk7AcptxcjiRK6NDRH+onpaTQ6/QICCQyCQyCAYfJQYWlglQ6RTqTZnvNdiySVoYWT8XJqBl2r99Ns6uZh9sfJpqMEklFUDIKRtHIt2/7Nn9w9R/k5uvl7pd54vgTKBmFKlsVdfa6nAOxt9yb28x1WRMcapjALeuJ6zIcLQvAjTcURQYfeP4BrJKVocgQAkLOQTmejvOH2/8wZ6z09sDbnBo5hUkyUWbUlO5ra6+dkxw+dvQxXrvwGm6zm5SSoj/cTyARoKmsKZclW2idLNbspRB5XI650nxjmLl+CxH4afOQv7kMBMBmA09ezNAlEtD17/837S9+l8E3fkrtUJiduma8f/LY0jeXlzbrXW6Vp9fHebsshJBR+dT1n2ffPY9d3j7TK8F595OKGaStSxfg0Fo/7j334Nh6/ez1ejmuVbHnWA5pXq6x0Le+NRUHZDRqcUAGQ3HK8kdNEq8AE7cSrkB81OuyhBI+5igR1Y8Jshv4M74z9AR6aPO0Fd6gF/EaQ+Eh9IKe4chwrvzMF/Vpau0l8mM32qm315NW04SSIXY27WQ0Opo7t81g4wenfsBgeBCraGWNYw01thrkjExbVRudE53ElTjBeJAefw9xOYaUAXNGII1KWVpkx5iNtJJi4PZrOD1ymng6js1gI51OI+pFVFSUtIJZMuM0OXOKrZyRMegNpNU0girgtrqpsWk9lBcnL3J27Cyfbvg0ZoOZRCpBNBXlxrobUTIKok7k2bPPMhrTMkglvVZi1uhs5J/3/nNB4pavmoDWbzZ48K+pHU2wM1ROnznJkw1+3nWEsQpGtt50D2k1nSs73lq1lcduf2zW9fjma9/kuQ+fI5lOUmYsw2aw8cHoBwA0lDVwd8vdABzrP5ZTzF/qfolAIsAd6+6Ycgg++Rb2V49x4E0Z1q1jz10hTDX1WKWpjWY0FSWRSnD49wr3mS1FHS0UtbMScTVzjSH/enT7u6lz1E0b25wkeY4NZNfjf86hsSO4f9KOQzUQsujxCwn2d1rwfucfl7aZ2LGDrnAfh1piuGU9DkVPSE3gd5vY/zevXF6iWlJUVxSLupEzR6xN+waJof1fmH2jaBHXasnuwcWe46MmXEshux/1mKH0eSthNq6EdVlCCR9zrDpRFQThN4G/B/TAP6mq+lczfm4EfgBcA0wAv6uqat98r/lJJKr5WKnYipmvc9p3mq3VWxmLjvHO4Dv0BntRVRW32c2Te5/MkYVnzjzDkZ4j+ON+YnIMo2gkLIcx6o0YRAO/uf43NYOlRJDJ5CQXJy8S7esiTgpFL1CdkHCldNRHRW6V18DXvsb33/0+6XQaq8FKKpNC0ktE5SioYDVYmUxMMhIZIZVJYZEsVFoqGY4MIwoiOp0Oh9FBRs0wEhlBySg4jU4anY00lzfTWtGKUTRiN9h5ofMFhsPDCDpNAQaQFZlYKsZ/vuE/LxgvkSNSXf30/OQHHK9KEjUINEb0+MUUkncT58VJvG4vBp2Bi+GLTMQm+KOr/6hgxusDzz+Ax+YhnU7TMdHBZHISl9EFgkaeJZ1EOpPO9SA/3/E8RtGIRbRQYangzQ8OExjpBRX2jJVRloQf1UxS07AZT+NVuXPF5BgJJcHPfu9nBd9btrx8KDxEIBHAZXJRY6+ZKnWe4zmrEVdTzOstmiQX2PwerOgj/A//J07/pVxJICgq2GMPoEL9AAAgAElEQVRpDsQ2Fb25nEYc/uYgPqcBS0anlYNn34ccxv6Nv1yy0rwklDZJK4ZF38hZrEK6iLLZJZfb558jldJIlSxreaV/9VfT18THLXLlSiCJpQqGEmbiSliXJZTwMcd8RHXZZkqCIOiB7wO7gAHgXUEQXlBV9WzeYfuBgKqqzYIgfBH4a+B3l3vuX2fMNLRZqdc5+O7BnCNjKpNiQ/kGULV4mUMnDuU2Q1XWKva27uW1vtdIpBJ8OPEhSkbBoDew1rGWXw7/kt3rd9PoasRpcvJ8x/O46iuIv/8OZkRa41Z+URZkxJBk+MZrOdl9mJHwCDaDjWAiiF6n10yb0FFvr+cz3s/w1sBbeGweuv3dGPQGzaVYLxJLxQgnw4xGR1EyCibRpGXBphNcDF3khrobMIrGnNnJC50vEE/HKRPLpiZCAFEnLujImu/c6VuX5MzNXoJDH6JPyOiMdsYbzGRMCSyCheHwMGk1jU7QUWGu4OTISeLK9Ggbb7mX3U27OTV6ivPB81glK/X2evrD/VhECxbRwq/GfkWjs5HWilYAXCYX8VScrvEujieOYxn1IWZg3JThx2sm+dxwGTVxA52jZxEqK6m0VpJIJfAn/NzeePuc7+2M7wynfaeJKlFS6RS9gV7e6n8Lp1kjjYVuhswyRinCVGY+nPadJpgMEkqENAKqavFL2V7s/PMv2qyogKnO4KuPUOcLgNWWe8yh6Bl0ZOBsX1FjzicOdY46QlUujjgGuXPcSW5ksoyjrJLBvPV1WTI1CxgBzUc4PpKczyvg3MUg/7MP5L62n2+fm6guYCw0DUVeq0WPo9A5/tt/g+5ujbCuXauR1ZnGTUswofpIr2Fvr0YS8zEzRma1sdhrXsKvP66EdVlCCb/GWIl4muuBblVVz6uqKgP/Cuydccxe4KlL//0scIcg5N+SLOFyIZuT+f7I+5glMwICMSXG1WuunhalkJ9/GUgEWGNbwzrXOhxGB3aTnSpbFaqq5jI3nUYnAZeZWFsrraobT0CmLVWOqXULL+nPgwrX1lyLy+QimAwSlsOY9WaMeiP94X4Odx3GaXLyyC2P8NkNn2Wtcy219lqqrFVEZc1lWMko6AQdgiDgsXmosWmEpb23fVqMyqdqP0VGzRBLxUAFOS0TlaPU2GuoueifNx4iP3KkY7wDa81aaFxHemML5jt2U1vTwlh0DFEQGYuP5dyR68vqkdMy49Fx7nv2Pn7jyd/gvmfv4+Xul7m/7X5ayluoslaxsXIjZeYyPFYP9fZ6EukEok6kzdOWK/NtrWhlIDRAZ6CTcDLMpJpkwpShSpZwpESOu+N8etLBmpCKL+IjEAsQSoZQMgojkREOvnuwYHbjhckLDEU0IqUX9Pjj/pzT9FyZj3PF1QDTcmxnPi/rFHzgJwe479n7+PJPvsw3X/smZ3xnmIxPohf0dE500unvJKWkMImmWedfbKZr9pz5Y6q11xLyuLSN+iWExDQ1IbXozeWs2JE7PoMnpuOkLaIdIMugKIR23Jwj0Zc1U7PIjMePLOfzIz53sVh03NBSMlyLuFbLjj3aswfKy6G5GTZu1HpAC+SdLhYf+TW8EmJkroTs4NWKoyphabgS1uVSUFpHJXxMsBJEtRboz/t+4NJjBY9RVVUBJoHymS8kCMIfC4LwniAI742Nja3A0EqYiSzxSKQSJFNJTKKJm+tvxmPzTNsMZdWsCksFA6EBBsODDIeHSaVTxFIxtnm2kVbTORLjMrtIq2m2XLWDyq/8GcFH/yv6ffdz3bbf4nOtn2OPdw831N1ATIlRaa3EqDciiRKRVASTaGIsNsZ4bJxvvP4NDDoDbVVtJJQESSVJjb0Gh0kr+xV1Yi5yx26047a4cZvdHLjuQO7O/v1t97Pdsx05LRNMBFFVFaPeSGJ0iDMv/S8OWs7SVW8pmB2YnwGYzSWV9BJpNU3XRBdD4SEEQdBMmtIKZtFMs6sZg95APBXnld5XCCaCNJQ1MJmY5OuvfZ2+QB/7t+/HY/Xgi/gwi2Z2r9/NXa13cVvjbdy76V70On2OkIWSIeSM5ros6kQQ9SioiBmwZARGjTId5hhGgxklo+Ayu9AJOm6su5Gt1Vvn3ECGkiHt9YDJxCSiTkTSSSSV5LzZsDNzgIF5N6zZDe35wHl6g71MJibpCfTwi4u/IJVJEZbD9If7c/nBg5HBWTdK8teq3WDn9MhpTgyfIJQI0X6+vSAxLjSmZlcz3dd7OVwV4vnKcQ5XBuk2RNg5IBW9uZxFHDZvZtuN9zBqFwjKYTJlDoL33oV/bdW0HueZmZrpTJpHjz46J7lfbRSV8zkXlrmpWda5LxNm5X8yQ8GfOQewKjmyix5HoWtRRN7porCEPOkVx5VAEj/q7OCl5t+u5ng+6WTnSliXi8WVto5KKGEerARRLaSMzmx8LeYYVFX9R1VVr1VV9drKysoVGFoJheAt93J3y93ctu42bm28Nafk5W+GdjbtpHuim1O+U1SYK9ChI5rSlM2W8hbMkpkae02OxHz/t77P9z7zPZpcTdOUN0VVcpt8j81DpaWScnM5BtFAVI6iqioROYI/7kdAU0oHIgNYDBb+fs/fs2/LPn538+9yU91NlFvKMUtmTJKJUDJEKpMCFeocddPUtPbz7Vq/6OZ9NLmacBgclJnKuOFsiLawhbBFz6GGCboqddNUhq6JLnwRH893PM/hrsPo0BGIBdALesKJsKbuxYMoisJYdIz6snrKTGVcnLzIyZGTdIx3AOC2uNEJOo1Em9w8eVLr/f3W7d/iutrr2Fq9lUprZU4hvL/t/mmq5UBogF1Nu/CWeym3lFNTsQ6boiMgpQlKaWRU4ihUNF7FhvINjMfGafO04S33zksCbAYbax1rkXSSlrOrl3CZXBglI1C8arMQ6cj+fCg8hEWy4La4sRlsDEeGqXHU4DRpkUSpdAqzaKbKUjXrRkn+Wt3ZtBOHycH2Ndtpq24rSMTnGtOx/mMIaxtga5vmMCqnECwWePQb824u89dTj7+HHn/PtJ+bN21h997/gv0bf8ng/i9gb7tuWsn3THLri/g4M3qG0ejoR6YoLlmpW4FNzbJVwqViERvpeRX8ueYAilKzF4MljWPm+1pJlSebJ52awJGfJ/3BB5fnGmZxOUnifOumyAqGFT1nFvn5t9m86GUq5csab4nsfPQ3L5aCK2kdlVDCAlh2jyqaglqf930dMPMvV/aYAUEQRKAM8K/AuUtYIhbqPfSWe6l11DIW05RtnU5HraMWq8HKxdBFzJJ5Vp9iob7amX2GdY46AnGtlPjs2Fk8Ng9j0TEkVWIwPEiTvokMmRzxGQwPUueoo7WilXMT5+gJ9GDUG5FVGZ2gw26ws7tp9/QewmSIV/pe4Y5GzTX3hc4XMIkmHBMfoDOU4VS0sbVXRvBGK6Cvb1pUjcvkIiJHmIhPYBEtOM1OJL3E+cB5UpkUdWV1GPVGfBEfg+FBKi2VbKzYyLGBYyQzSdaVrSOUCNEb7GU8Pk4ileCxo4/lCGn7+XYGQ4PU2Gv4fOvnp/WzwpSJUCqd4oVzL4DFgrO8jv7wAJFMita4FWFbG9FyOzevuZqfdf2Mo71H+dXYr3CZXLRWtFJprWQwNDjtWtxQdwNH+45S76gHFWJKDDkts865Dlig/zMP2WuSj6yzdP7Pg4kgekFPV6iLaCqKP+4nkoxg0BvY6tlKQkmgomIWzfOef66evadPP43H5mEwPMgvh37JDXU3TOtndRgdHO09yu3rbueammvgN7THg4kg7YYUc3XWzexJTSpJfn7x5wBFu3DPXPcd4x3o0FFtq84R6ex7W3KPX14kQtfGKtq/cC2DDa45ewcX3fObRf6mBqa+Pv54bjO2UO/ivOderWiHfGOh/I30HJvIrIJf8PP5+H9acA5WCisyjocfniLS+cZNxao8+dckGASbjdq0lZCo4EQzJePIEULNdUX9zlgxLKGvdhYWWm+LXDcrgmLP+VH1QxaasyJ+Lyz7HFcy2cvHSqzLy4lSX20JHyOshKL6LuAVBGGdIAgG4IvACzOOeQF44NJ//w7wqnql5uJ8QjBX72H+5jKVSXFn853s27KPL27+IpWWSpKpJMlUctqxhXoDs5ipDtTYa/An/CSUBHaDHSWtkFSSVFgqMIgG+sP9OE3O3J367AbXY/Nwd8vdbPNsI6NmyKgZNlZs5IldT5BIJwqWWT5x/AnCchhBEBAQOLZWh0+IApqhzpBRhliMl7fa+OpLX6XH36OV3aZl9Do9d6y7g13rdyHppNym+paGW9havZWWyhYsBgtbKrew1bMVFRWHwYFdsjMWH+Ps2FlGo6Ok02kskoWfdf2MP3j+D/i743/HaHQUvaDnjO8Mjx59lAM/OTBt3rLveVPVJu7ecDdm0UzEJFBX00pT43Zc196Cqa6Rm+tvBiCYDBJMBnGZXSSUBMf6j9Hj75m1gdy3ZR8t5S1k1AxmyYysyLhMLq6tuXbB/s98LFSamP25TtDRMdGBnJGRdBJlxjI6JzqR0zIbyjcwEZ/AH/ezoXzDvOcvpMbFU3GOnD+SK/U1iSaO9h3FF/FNGxOQe64v4uP1vtc52nuUFztfnFPNnKnOesu93NJwi1YCP8dnZSZmrvuRyAhpNZ0zzMqOa8lqVJ6a0VVv4ZC9m/B//JC6i5NzqrWL7fnNYYEy0mJ6F+c8d7+0eqrMHKpB1z88Nufvq5ll7rlr3NurueieOwdnzmhfU6lV29hNG8d4I957/5O2eT9+XDtvPgptMJej8sxUyiIRGB1l53nwG9IERYWMQSI4OVr074wVwUqUmRajAq6E2rTYsRZ7zo+iH3KuOTt7duXKy0vq7OXFx7WvtoRPJFYqnuYzwHfR4mmeVFX1LwVB+BbwnqqqLwiCYAL+F7AdTUn9oqqq83q5f9Ljaa4EFBMlUkyUQtdEF0+ffpq3B98GwOv2ctJ3EgGBsdgYakbFJJlQUQknw/z+tt/PRc1kld/5Xj8/xuTs6FmO9R+jN9CLXqfnvs33MRYbI67EiQz0Eug9S1VKwoCereMi9/dY+MoDlfToQ7jNblKZFLIiU2OvocJaQbOrmRp7Dc+efZZqWzVZD7B4Kk7HeAdry9byuY2fA+Ds6Fl+3PljInIEo2gklU6RVtM0u5rxJ/wY9AYayxqJpCLEU3EknaRF7pBhS9UW9Dp9zqhorvfcfr6dsBwmqSR5Z/Ad3us9RiI6CZkM1WkTuooqBJsdp9k5KzM2ey2ycUWiTkRVVdJqelERSMVkoR46cYj3ht6je6IbOSMjKzKV1kokvUS9o56r11xd9PkLrcPD3Ye1uB6vtvH2RXy80vsKLrOLO9ffmRuTWTRjlswklSTH+o9hlaxkyADQUt5SkHCuVHbskjNhZzy/oEqZF4lwcO0YYb2CM5aBsjL42tfmfO3Fxl51TXTR/tXPMpiaoDZtZeeYDW/MPC1+odjfE9nfAQICn4q72PejbrxHT4NOBzU12thh5aIdCkSJdFniHKoawP2n//viol+2boUPP9TGqtNBJqP927gRTp1a3jjnw8xIm44OjaiuXbvy85XFzLiNc+cgmQSjka5t9bRXRhhKB6jxK+wMuvBWtKy++rVSMUxzRIl0rXPQ/s3fz8VP7YxVa+s8i8VE0CxlrMXG3nwUcVRzxa+MjEB19eJiWeZSTT8hES9XjPN5KdashCsMqxpPA6Cq6s+An8147NG8/04A967EuUq4fCgmmqTYKIVEOsHt627PvU7nRCdtnjYcRgfvDL7D2bGzmCUzDY4G3h95H1/Ex53r7wRg//b9PH36aY72HgW0EtYsuia66PH38Gb/m6iqSre/mzJTGXqdHp2g44VzL3BL/S2MRcfwGaIIdRXU98v4lTDD5RaevvPTpIzDuJNiLn4HyCk/n274NDubdvJ8x/ME4gFcFheJVIJoSnMRTmVSvN73ei6X9Nqaa/nFxV8QTUZxmBw0u5sJy2GskpYd2zfZR0t5C+cT55HTMo2uRmKpGEPhIbZWb6X9fDsHrjswb4nw48ce55TvFIHxAZRQAJ0ACT2MksQ2OkBacTIWG+OZM8/MynXNQkWlylq1pD+U85Ym5v38zYtvomQU9IIel1nLjY0mo4xERhZFjAutw/z14Yv46BjvQM2onBs/xxrrGrZ4tuTW6aETh+gc78QiWlBRiafi3Fx/M0bRWLD0dsklsgXmKUvcnz79NEfOH6Hb38226m2YJfO8MT+zInGSoWnxUfmlW4Mmmbq4BAYRxsdz8zSz9Dt/TMUgN4YdN1P37y8QMsc5VJ9k/zkLXlnIlZEuVAqe/172tu4ldOod/C89B2NuSKe1DfnFi9DQoJGvlSpBKxAl0u6YwG2pXFr0i6pqRCL773IUBT3+OF1ulfaGGIOmILUeCzvf8+MdGgKHY/ElvcVgZlmgxwMXLkAigTdqwtsbgpG4RlKqqugK99H+f32Jwf6d1G7/9OpsvleqzLRAyWNXpY5Dlg7clyoCQlUuDiVH2D+cR1YXozZlx6oo0NWlkXyAe++FysrCZa3Fxt4sMo5qTiymzHauMlGjccpNvZjy8vnKmz8BpagL/k6/nFipdVRCCZcBK0JUS/j1xEKEBBbepEJhMtvmaeO07zR3NN3Bb234LVorWjnef5xgMojT7OTO9XdilswcOnGIOxrvmEV0s4+/0veK1veaCHBq5BRyWsYoGkkoCURBRBAE3h95n0ZnI4FEABwGzDu2ckdFK0bRyNHeo1SbqrmgXKA/2J9zCFYzKm6zO7fpeujGh3ji+BOMhEfwWD00uZq4OHmRjvEOJJ2E0+wkEA/gT/i5d9O99If60Qk6zJKZ0ZFR9Do9kl4ilU5hkkzIaTmnzppFM8FEcNq8zUUosr3Dbw++jS4QwJoRSYoqprRARgVZr1IeTCA2eWfluq7kH8qFCE92nDaDDbfFTTgZpjvQTVpNMx4b59mzz/J8x/M8dOND7GreteC5Zq7D7PrwRXw5pdRtdeO2unGYHNM2y/u37+erh7+KIAi4TC62V2/HY/PkVNKZWOgGzWLuiufP+Z3r7+Sk7yRHeo6we/3ueed9wRtAeRvb2oRB6x2MZaCiApifWBc7/twYtjWBaMN55AhMjtK+oQzv7303t6mZk9hfioJqN5zCXeXCecdnYPNmnK++CWkD7Q1pvJ2mqVJWn08jqitVglagT3NQCFM3qYf//t+1udq9G8dVmwqug2kIhTQVc3Q0py5SWwvh8PLHOQ+6xjs5tCWNO6WnLi4Rsuk49GkH+4/F8AYCq7PBnEmayso0shqJaApfJKKRVI9HU6gbYrijAnWvv0/oqu2rs/nOJzKTk9paSSa1Tfbhw8W//2JuXtzxGXj2X2h3TOCN1i7+ZkBvL+j10N+vqe+gPV+WtXkbHoYHH4SqKm1drVsHt90GT11K8FuI9C23H3KxPbhzkehNm6Z6VYshO/PdbPgE5NMuKx95NfBx66st4ROLElEtYV4sREiKUZ8Kkdn17vVE5WiuR7bJ1YRVsmIxWKa9FsCTJ59k+5rts37B5z/uMDr45dAvARiLjuEwOAjJIZLJJIG45txba6/lNxp+I+dynFEzCAik0ik6JzrJqBlQIaEk0Ov0fPGqL+bee6OrkRvrbuTtwbcJJoK0VLSwsWIjDWUNDIWHCCa0HtGrqq7CKllJppN0TnTiVt3odDpCyRC19lqcdidjkbFcL+y5iXOUGcswiSZe6nmJRCrBwXcPzkt+UpkUNfYa3CPdRCxW3nRFMKYhJqUxZHSocTmX65o1pfKWe5f9h3KxZUtOk5NAIkAsFWM4PIysyIRTYcqMZVTbqgnEAzxx/AkaXY0Lnn/mOswSwGKU0qzL9czy1LnI3Hw3aBZL9vPnPNvz+v7I+7w18BZV1qrc+Wai0GcmnorzWu9r2vx/aSM7v9uFNwo7x2wcWjMCgopj1y5Cl/o/C6m1ixn/tDFs3gybN+PIkvsdUxucgsT+g/f4/MHjkLQxuN1AnT+iOcXedx+Mj+OwWhg0yhoBunhRUyiTyZWNdpipGtjt1MZEQo4UTqs1514buvcuatqum/+1shvpDRumHsuWJmaxWDOYIo5v32LDHQvgFDRnbqcigirRvqMR79OdRU3DossNCxkxGQzwzDPa+LJlqmiGdG5Zr41vfGL1Nt/Z+VeUqfUiCBoRLESy5prbQjcvjEnqduQVfG3ejEO9n8E3XoSzS7gZsG4dvP32VJl4VnXU6bQbHR6PVvkQCkFrq/a+nnoKHngAXntt9RWuxarT8xlzLYbszKeafv/7yzP/+higmJv6JZRQwmyshJlSCZ9gFGPQMpf5zhbPlmnGJYqqEE/Feb3vdX7c8WNe73udeCpOf6i/YLRF/uMem4dGZyNGvRFRJ6KgYJbM6AU9ekHPSHSEekd9jqRmx9Dsbua94fewG+3YjXZEvYioE9nRuIOLoYscfPcgX/7Jl/nKz75CNBVlb+teblt3G3Elji/qY717Pbc23sre1r3c2ngr693rSatpHrrpIW5vvJ3x2DiRZERTUkUTTqOTjgktxsYm2ZhMTPLh+If0BfoIxAPcUHfDgtEltfZaTKKJhN2KmlYwpAUmDWnigoqoqKxPOzDoDThNTuKpOC90vsAjrz7CC50vEE/FZ81jMYY++YY5kk7iSM8RHnj+AR47+hhdE10FDbXaPG1sqdqCWTTjT/iR05p5U6WlUlM3LS6UjLKkHMZcHrCSIJFOYBbNBfOAs1iskdBcpjqLzQPNN4LKqr8AAsK813nmZ8YX8XG07yhGyagZFnnXcujAjXStc+AdiLM/3Iz9t+9jsME5r9nTYsa/YJ5n3lzNMmb78QW8SZum+CYNhCx6EEU4cgQqKgipCWqSBk2ta2jQFCidbuWjHfKjRMrL2Rkqx2/VTRkCmQX8b7YvbAi0UFZisWYwWZOd6mq45x6tNDTv+K7n/mna5+jMzc044pkpsiPLOOIZhm69uqi3X4zRVcE5m8+IKc+IZdAk41D02vguqfmrElmTnf+hoeml1zU1s02H5rsWBd5b7V37CHnrp50u5K2n5sCfLS2CJjtWVdX+ZbSeeCRJuxnj82nrXVGmGye99trqxN7MxGIzdlcqfmU+A5+PY8TLIlHs79MSSihhOkqKagnLQjHlwcX0ugKIgsjRvqO5jXRciXO07yhlxrKCqm29o37a4zfV38Q/n/xnBATMgpkMGSS9xDVrriGWitEf6mdDYsO0MbjNbqpt1aQzaeJKnGprNWWmMs1VtucIe1v35mJWzoyewWF05MjuheCFOdVkb7mXfVv2EVfiuM1u4qk4J0dOcmzgGPX2eq0cVg4TlaPIaRmdTscd6+6YRqTnUiV2Nu3k5MhJTqx1Ehz2Y1YgJoKogjEDicYG5FQUq2TlX878CyklxVhsjInYBN2Bbj7T/Bmuqrpq2ngXQpbgJJUkxweOY5WseGweTo2eYujYEIIgsN69fppKd0fjHVyYvMDW6q2oqso7g+9g0BuotlUDkEgl8Fg9S97ULkYpLWadFoPF3hXPrzjoGO/AIlkQEHAFYjgPP6mV0pb9aFopLcz+zLw//D4AV1dfPRVvs/la2q++He91B/DCnHE7Sx1/sZ9bKFB58eHf5NSTnWM2DjVMAHoc42OEvrAX/+Hn+PwFm7aRF0WNuK32xrS3F6/bzf6LZtorIwyaZGoSJj7/XmbhdbBQT1cxKlV+yWU0qhGY0VEwmaCsjC5bkkM//RbuB7+S+xz12BWsn78N7xsfaCpcRQWhHTctrABfwpKrKOZTyvIUttq4REhN4FRU2L0bgB5/DwOhAR559ZGVNYyx2aaIjtk81dOsqtNJ1kLXYsZ723mJzMPC67wo7NmjlcV2d2tkNHsTRhC0cSWT2n8bjVPPuZz9mEsps12JcuOJCW1ODAaNiErS9Js9v+alqIv5fVpCCSVMoaSolrBszBnpkPfzhaJwgFzPpoCA9n/t+/Wu9QXVsAe3PTjt8VpHLc2uZiS9RFJJ5vI6XWYXTa4m1rvWzxqDoio0uhqpK6tja/VWvOVeKq2VdPm7qLJV4TQ5tegXiwuLZKFjXFNDHUYHbrN71ri6J7oZjY7yyKuP8OjRR0ln0jhNTtbY17DHu4cqSxVyWtbGa6/l7pa7aa1opdZRC5BTk0+MnOCM78yc8/3QTQ9RWetFqPZg0hvZPmFgW6wMtbwcnyXDBvcG3hp8i7gcR9SJWjmzoCeRSnC4+zDD4eFFRdJklcGO8Q6skhWzZMYiWpAVmbH4GKPR0VkqXXegO3fdXWYXkihRYakgo2b41eivODlykrHYGHpBr51kCfETxSilWbX3qVNaD9iXtn6p4DotBou9K54/vkAiACpEhy7Q+sopmJzEYbAxJE/MUt9mfmYSSoLbG2+fdiNjKcrVYsZf7Oe2IPLUE2/MzP6L5dhjaQZr7djbrmP/vifw2hun1JMHHpjqVVtq9EiRY/LGzBy4UMm3z9Vy4EMr3vINCz8XpquzMxWvYlSqfAIlyxpBFwRNYQPa6xXcvsi0z1Gbp43TljDBLz9I5i//guCXH8S/tqroSJhC0U7LVjzz1K+dPRn8bhPBe+8ic9Umuia6+PnFn1PnqCtewV0IWYIvyxpZNRg0E64sZpKsRSqGy1rnhca6Y4d2TTMZrQ81OzZF0b4XRe1nnqnP8mXtx1yoOmClkX/91q7VHrtwQbuOv2aq6XxY0XVWQgmfIJQU1RIuC4pxG01lUtzeeDvnJs4RjAdxmpzc3ng7SkbhS1u/VFANa3Q1Tnv8r3f+NU8cf0Jzms1z6G1yNdHkapoV11FrryWpJDkzqpFCs2gmEA+QyqTY5tkGgMukvU7W9Ai0zf3mqs3sbNqZO79e0CMIAmbJjMfo4Xj/cfxxf06F9UV8hGStN3VDxQbiSpxj/cdQ0gqCIHCs/xgWSevRDcQD+GN+uia65jRV2r5mO3e13MyHFKQAACAASURBVDUtRmU4PMzbA2/T5e/SlDuzC1EvYtAbkPQSOlnHZGKSH5z6AdfXXs+D2x4s6g9lluAEEgHNwReIK/FcHyozDFCzKl3+dX+5+2W++fo3Oe07jcuk3TxIZVIMR4bpeu6f8D78neINPvLmYT6ldKWdFhd7Vzx/fKqqoqJy88kJPIoJDJoJkl4vcXBjlMF/P0BtxZ/lFKj8uctGwOSjWDU8v0dR0kkMhgZpLm/Oqfy+qI/dTbsLrrXFuARPw4y+Nu9YBu+gFf6378F1e+A64J4/1H6+GHOXvN7Dro1VtH/hWgYbXEvvvVypDXoxKlV+j57RqJlI6XQ5V9hBMUaduXLay87s5V9sJcBKOVjPwiX1ywvsz66v0CADoQFuabglN74V6VnNJ/jV1VqPKmjxKKKoXcPbbtMIYm8vBIMaKVwEEVzyOs9H/jqurdUUw/FxTfXduFE7JhyG5maNyIqiRhQvdz/m5XZ8nalwO50aOS4v/8SQ1CxWZJ2VUMInDCWiWsIVg1p7LWE5zK2Nt+Yey+YxzueCW+jxmQ69ep2+oAqRJR5bqrYwEB7gV6O/YiQ6goDAcx8+x22Nt9Fa0cqx/mPEU3HKzGU51S67YcwnE/lmUFnDoI7xDjw2Dx3jHVRYKpiITZBQEpglM4lUgmQ6iaqqOIyO3GMZMrR52ubd4BXahJolM3e13MVgeBCXycXxgeNk1AypTAoBgcnkJGXGMgRBIJFO8O9n/70oM6PsPBn1RuJyHASIpWJsr97O+yPvzyKqhTbDu5p38ebAm5wcOYmclnGanLRecl9u/x+P411i/MR8f/yXYyCVJXinfac1syyTiy2eLdzReAfdge6iiUN2fLk5nHidjMFGSFTotiYQELCkjdQNhgldUqBmEumllo0VIuqCIDAwOcAvh39Jla1qmsP2it3hX8xmuFhzlzwi0FVv4ZC9G/d/dFH32/cT8joWHv9qbtCLIcH5ZDZrIgUaWf3wQ2r1CUL1Es4PPtDMq5jey78UXI5yw/zPXzaDOB/LNozJJ/jZnuaREUgkNDU+65ibvdGhKNrPQVMwLxcRnLmOPR5NAS6UBZq94TJzHS7WkGupuJxltp+A6JkSSihh9VAiqiVcMVipTdWu5l05pXUoPDRvZuc0RW5ykMnkJBvKN+AwODg7fpYfdfyIz7V8ji1VWzQl0OjCbrAXJCcz+/8qLBWcGDnB2fGzqKpKb7AXh9HBrqZdjMXGcqrxOtc6VFUlmAjmHttWvY1Ka+W8G7z55qv9fDvD4WFCcghJkDBJJsZiYySVJOWmclwmFwIC5/znePr00zx2+2Pzzml2np458wxHeo5QZavixrobMYpGKs2aOVI2Yme+65bKpLiz+c5pKnBGzTAY84Fl/fSDV2Azs1SnxSzBS2fS9AZ70aHDH/djNVi5MHlhfkJ0abPZNd5J+xYbg7dencuX3L99P+1lP2JQnqAmbaU2bsCc0V2Klqmck0gvtce2EFFf717PieET7G3di7N7AP6fH2rKj8dJ+0AY7+9/f9ZcLDakvmuii/aKPgYfuYla+73zP6fYjWweEWivHMOtmnCqGXj5ZZxbvlZw3mZhtTboxZDgfDLrcGgkanRUI1EmEzvlWg7po/Dsv+BQ7yfkrV82qVyp3uxisVBU0ZIIWKG4HFGcIoA7dswmiPG4RlZHRjSy+Kd/uvrEbDGErNA6XGxszMcFn4DomRJKKGH1UCKqJVwxWMlN1WJKbLLHvnHhDTZWbMRt0TYbW6q20BPo4ZXeV/jK9V/hj6/546KjenwRH50TnVSYK4hJMXxRH6PRUXToOOc/h8vk4oa6GzCKRuwGO8AsU6BgIjhvid5C8/V8x/M0OZsYCA8QS8VQ0gp2gx05I7PGtgaLpMW6vD34dtHztG/LPgDeGniLtwbe4lO1n+LhmzWlopjrNudG1uLRNi8rvJlZauljluCdGjmFRbJgkSzEU3GGwkNsrd46NyG6tNnscqta/mUsQN1zLxPSmzl0ieAeaLkfvvMdSId4ZAd4FAuIUs6MZi4ivZSysbmIen+on1sjFfCv/6pt+q1WHP4Igy8+A57P5jbGSymdXvRzit3I5hGBQZNMXVwCg6iRbC5z1MNcytd8hGImmfV6tfcjy2C1amW0w3HaHRMMvvEiNVf92fJI5aUxent78WbHuGN1Cc9CUUVLImALqdUzCeLk5FS+bVubdvxTT8F11xU+30qpmPkROtmcV1HUSn2LwWJjYz4uWM2S+xJKKOHXHiWiWsIVhY+yh6M/1E9DWUPue7vRTpunjf7J/qJK75pdzTxx/AmUjEIoEdIMhwwWdq3fxXhsnG5/Nx+Of0hDWQOpdIrB8CAt5S08dNNDAEtSk+criV7vWk+5uRyrZCWaitKT7smRLodJM1gRVCFnWrUQ8gnI3ta9uTHON46ZmFMF/u2H4a3vaAet4GZmqSp9luAFE8EcyTVJJoLx4PyE6NJms70hhjt1KV9SlHG++iZ8+UHaX/wu3qf+Py3KIxikNhAjZIrgvOUz08o9VyqyYC6iXu+oJ/TCYZyiqG2OgZBFT03MOG1jPG/p9DvdBTf4iy63LnYjm0doaxNaf6+mRFcsat6WohBPw3KUr5lkNi+TFDTjKW+0VsvvfHJp5b7ZMXb9+R/TXpdi8EaB2tBZdv75H+PlH1eV9BS8eZYXVQQsnoAtpFbPvNFxyZwKs3kq/mWu862kivnww/Dgg9qNE71eO3cqpY0nG40zH35dS2Qvd09sCVO4XKXkJZSwiii5/pZQwiXUO+oJxoPTHgvGg7MUqULomujilb5XaPO04bF6GIuPMR4fp6W8BYCXz7+M3Win3FKOIAh0jnfii/g4O3Y2l2G50o6AWzxb2LZmG/va9vFH1/wRNzfcjEE0YDVYUVGJp+L4E34+Vfepol7v6dNP0znRyet9r/PzCz8nqSTnzOAslKsK8zgf3vOHq5Kjt1SnxXyCF//gBPzbv5H4f5/B+eLLhH763NyE6JLjaC5fErRN8Pi45rb6xk+17z0eaGlhp7EVf3UZwZ6zReW7LhZzuSI/uO1B/NExghYdGVSCooLfkGZnqHzaxnhO19gTb8yZVbkkp1mbTTvv2bNzu4HmuZXuHLPhFxIEhSTDt13H4a7DPN/xPL6Ib16H2SXlis5EvvKVn4OZn+VZLObLloQlOWEDdP3DYxxqiRG26KlLSIQteg61xOj6h8cWP8a5zjHPZ3yaC/yHo4vL7SyE+RyXZ7rYJhLa13wzpfzz5c/pvn05RXvZ13LPHq2c22DQzm8waC63Lldxr7fQWlgKlrh+VhzzXb8SVgfFZjuXUMIVjpKiWkIJl/Dgtgf5+mtfB8BpdhKMB/En/PzpDX+64HPzVSRvuRdBEAjEA4zHxhmPjZNRMxj1RqxmKx6rh1gqhlGvlf2G8wx0lmqaUggz1cTWilb6Q/2Um8sJxAIYRSMt5S25ct5CyDcUeqnnJdZY15DKpOjx93Bi5AQ71+2kzFQ26zkFSz9DzXi/98zcpYir1D+4FJU+O3c1H/RxpvtDEiKkddDkU/Afa+fztGrOtTNxSd3JKX7KJVfSigpN8RsKg6V8amwxM/uHq3nGPchrHS+govKp2uJuHBSD+crDG+OttOsuMOjIUJM08PmLTrxjmWkb4zlLp19/f84yxdq/vrf4cut8RWvTJm1jHokUfjN5yoy3r4/9tmae+XwzR3TnqRKKM4VajrlWDiupfM2nJi9D7WvPdGs9vIr2J96piCCaaM/0FJW3uxD+f/buPbit674X/XcBIMAXQBKiRImkJZEWZEmR33JSJ5NEZWjfsqnto9SZObY7dWydyR3mqL1OW7ZujyZxrNtaPfJJMqlcTjOhE+c2ds+J69M6jXli04rzcBzHih+RIlmCTMqyQImU+ALFB0AQ+/6xuIkHN4C9gQ1gA/x+ZjQwQTzWfoDeP/zW+v10T+/u75dVeM+flxnOpia5xtTMNYrJGbuaGvnFR13c3yX1/ZL36fnz8ndLvWwB5JbFDAaBbdtk0KtK7vOaitlTZMt1zSvpU65TyWnVEYqiZH5UEezatUs5evRosYdBq8xLZ17Ck28/ifPB82j1tOLBGx7EbVtuy/g8tdqlWiRo5MoIfn7u54hEI3A5XBgODmM2Movtjdtx4coFLEQXMDU3hTXVa7C2Zi1cdhduWH8Dvrz7y6ZujxpoqkWltjRswZmJM5pFppKnRG5p2IKXz74Mb5UXb118C28E3sD43DiaaprgcXkwszCD0GII9+28L6EYk9pKJWG97du/hPvZH6D7bGPiRViBL5qMTPv0j/kxsOd6HKubw0SVDd55gZ2XbegcVORUxomJlU+KX6O6dRbe2Sg8c1EEP3snxjetw96v/hi+oWDCeky/GEffdRF4H9yXMD3ZSEY9q+ms8ReyKY5JfECSMLY/+//gq1i/8oJ8YgL+N36k/Ryt7enoWLk+dWZGu1KqBs1zbalSuNaXPlqf05OXTuJc8Bzuu/Y+fYWifv8jGFg4jYB9Fi2Ti+gcdsGneOWaUx1jXiFVBdgc9s3+BzejdXQeNqdr+b5oOITAukocePKs8TEm0bXf4/pn+hcuYmCzgoBbQQs86LxUC99jeZqGnO68PnQocZ+ePi3XkrpcwNalfroGzr8VcjyfU54L2ch1LFTa1GUFGn+jMThYvHERaRBC/FpRlF1av+PUX6I4t225Dc/c/Qx+9uDP8Mzdz+gKUoFY5knVVNuE65quw7qadVAUBY3VjWiqaYLdZsfswiym56cRDAfRUNWAhipZgffF9140NgVRh/hpeJ3tnbKtynRAM0hNnhL5+GuPYzG6iPrKernm1lEFm7DhSvgKIIAKWwXCi2EIkbjGVXPq55FXMVyrmDPFLoVUUxHjf29k2qdvjQ/dvwjjH1+uwjM/rMQTL7vQ/U4FfMEK+CuC2u+1lN3xuTdj73EH3NUNCHzmNrivu0UGan/ySOI0xZkZDDROw/vRTtRX1sMmbKivrE85pTrVdmc1nVXNRKWZbp1y6nTjNSmnKRqabr00VTqBgYyW5rnm/wDDvX+vOd0x/nM6cmUEr37wKibnJ3FV3VW69pt/zI++jzgxPT+F1okIpisF+rbPwx8alm1SspFqWmQO+6blE59GUITluQYA4TCCIozmT3w6uzEm0TW9eymj42/zoO/jtXIachCYjs6jr/tW+D+ss8iQUenO6+R92tSUOF14Zia3LGbyNGSjr2fmFNkcP1tU4vIxlZyoCBioEplAay2g3WbHo7/7KL7R9Q3satm1XOV3YXEBwXAQ29Zsw7qadbKYkQDW1a7THZwYlSmYiZ8SqQZLC9EFnJ8+D0BOkQwvhtFc24xFZRGzYfk/wJ1rdyISjSS8V3LQDgDBqUtoXszfRZOeYG15G8+ch+1rX0f9o/8d3u8+i4EffD31C7vdwOKirOQ5Pw/MzcFfNYe+m2yp32vpYtP3znl0f+8UDnz+GblWb41P8yI6sKcTnus/nPC2Gdd0xtE6droDXR0XxivWHK7xpb4g370b6OiAb9ft6P6r7+NA6KOx52itl8vxYmrFuXb8OIL/+j00j85rrsuK/5yevHQSNtgQRRQ7Gnfo2m8DgwPwvncB9bZq2KIK6q9E4J2JYmBHJfDKK7rGrFsO+6bzjocw/gedmPTWIDpzBZPeGoz/QSc673jI2BhSrHHU/IwnT+9eCpQG1l6BV1Sjvn49bC2tqHfUwLtzV97+1gFIfV4n79O6Ohms1tSYszZex5c/BcNAZXXL9UsTIotgoEpkguQs0mx4FlWOKjz1zlMYGBzApzZ/Cu0N7djSsAX37LwHLe4WrK1dC0VRMLswi9mFWdzQdIPu4MQoNZgJRUL42fs/w0/O/gSnxk7he7/5HgDtDMn6mvUYvTIKANjWuA12mx2hxRBa3a3wrfGhqbYJO9buWLH2ULOAT1MtOj9IWhJv4kVTcrAWioRwauwU/rT/T5cznoHpADz+D4BnnpEtLJZasgz/4OnUBSa++EUZpIbDQDQKKAoG2gFvxCED3iwyoMkX0S03fiLzRX8aWRUvypXWBfn998s2IFrFO1IV9ti9O6eLqRXn2ssvYLxayKJQGpn7+M/pueA51FXV4WNXfQxNtbLwTqb9FpgOwPPBCEZss/hJm8C/7bDh7WaB4+45WQjKTDlcaPrW+LB37e1wL9oRqF6Ee9GOvWtvz666sXrM/H7gM58B1q9H55e/g/HjR1cU6UooArYUKCUUFltar5338zMVrX3qdAJPP70yqM22EJFVCgcxUFndrPSlCVEOWEyJyCRq0Z74dX1NriYEQ0G8fPblFdMf3xl9Z7n9yY3rb0zoqWq2wHQAFbYKvHb+NVRXVKO+sh6zkVm8OPgi7rvuPs2iOS2eFozNjWFyfhJra9bi1tZbcWToCOw2O1wOF65uuBp2m31FhVrNAj6f/hJ8rzwGOGfy0ksvvl+oOqWzqqIKAmI541lpr0TwSOaWLAm+9CXgO98B3n9fBqp2OwIbqtAarQFefHG5pUwuPTyzbaGjMtorNuf2LKrk4lcdHamLdwDav3vllZxaV6w410YnsGd2PXyzVbEHJWXu44trJa+zzPQFQYu7Be/VhHHMC9REBBrmgQkXMFYZhb9yxpRCRcsMtPVYcUw/qICv5zH4nE6g+mr5efvlY0BVi/4L1fhiLFNTwKj80gozM/ANBbG39zUMPLQWgY1e7d7JS8WBWqYUBKsXZSuhSAS4/XZTWzAZoneflkMhIraFoTwVKCQqJBZTIjKZnkIjKYvU5NiSJt2YXhx8EQIC1RVyCu7cwhyiShS3X337crCUPJ5Pbf5UQvGldMWYMjKzUIjG9qn7/Cdnf4K5yBwEBCodlfjk5k9icn4ScwtzmPuHr8Jrr4UnYkfQsYhx5yL2nlsD3/m51AUmkopS9G66hGl7BPVTIeDv/g5A+gI+eiQXvTKyX42cSzmfd+n68qUr3qEohSnsYaCATDb7wj/mx76//BDsoQU0hATmHAKzFQquHVHQvuBG95Gg5vNMlXQM/PvuRZ/nTOJ2PHkYe3/jkEWeVEYL6cQfz9On5dT3xUX5hY3bLafNZiog1d8vW+VUvwtv9Vp4OroQ9F1l6t860754icdCRInYj5OI8ihdMSUGqkQmS64sCgBRJYpAMIADHQeW7zManORyQeYf8+P+f7sf62vWo8pZhfmFecwszODW1lsRiUZwoONATsGS2Yxua3zQ8crZV1Bpr8RsZHZ5Wqe6///48RcxYHsfwx4BuwIIBViILqClYg06v/Ef2u+RdNHqr55D34aL8Loa4PnTv8j7lwx66D12vW/0Yvo3b6D+yC+Ay5eBxkZMdnwU7utuyRxkZ6oSHL+fpqaAkREZ3NTUABs3xvpVqmZm4G/zYOArnzMvyNBRyTib/Rav+/PNmJydQNC2gPrZKLZNOrAW1QhsrDelom5aGtvXu/kypu++A/U3/M7ywya//DDczlp0n1sXe67eLwbUoOS11wCbDWhull8uReLWottsMoBtbAQuXsw47Hz9bdH9ZYPRQIsVU2MMfqaIiIxioEpUQEZbZuhhRgb2K698BW9ffBvhxTDqK+uxrXHb8nRjM/u35irbbVUvhp8/9TwqHZW4acNNy2sPl/f/5c0p28e8t64CG2o3IKJEEoMmjQs1v+sKBh66E8NL0x6LGdQbsf+b96D1uZdgc1TI7QmHEY0sIPCZ23Dg88+kf3JHh1ynODUVa+kRn1WLa0eyPE1UUWSxmkhE/ndDQ8I+7Ou+Fd6du8ydVZCcud+9W04xNikb1Pud/4rpf30G9YpreR9OihDcf3gPuj/3RPbj1kMj07e//SxaQy7Yat3LXz5Er0wj4AzhwNDm2HP1ZATjz/WFBTnlXYjY8QNiwZuyVMU7Vd/bAjDUJkcr0AK0A1hmVGO4L4goz9iehqiANIsJJRcaMSinyq5L7r32XlzTeA0+ufmT+Pimj8PlcOU8Lj0ytY1Jlu22qtVpv9H1DVzTeA1cDtfK/b+0bmtgawW8U2HUV3thu+dehLa04dTYKbwz+s7KSr4aRSl8j30T3Z97IrESbrJsi7HkUctP3kSwyra8RhdOJ4JVNjT/5M3MTz5xQmZJFxYAh0PejozEigip++nKFSAahb/Jgd7OOuz/RAS9Nyvwt9cl7MOBh+6Ed+eunM5pTfHFbHp6Uhd4ypJpFXWzodFypGVaIDh+YblAGKam5BrQsSwK6cSvS62vl4F+RUUsSAXkf6s/q+1visRIm5wVrbEefli7wFd/vzULEZn990Tv68Wfc1NTchr4e+/JjHumMVjwbyARlRYGqkQmM9RHUiczKruaOS69wWc2PT5z3daM29nVhcDez8LzyN8Bf/ZnwM6dePfyu/BWehGOhLWDJqOVPFNVuS3yhVrnsSsYr7Zh0hFBFAomHRGMV9vQeXwm85NDIXmhb1v634Y6/TMUij2mqwuor4f/1q3o+4Qb07VOtM5VYLrajr7mi/B//5+W92FgY0Pm45zrhW6qICWH/r2+NT7svftv4f6TP0fgr74A95/8Ofbe/beFyahrtBzp/M00xmvsmKy2yWNabcN4nROd8xuMV/xMDoTrlr5c0GKzAXZ7jhuUGyNtchJUV8uAK9W5YbWKqWb/PTHyeuo5NzUFnDsnv6Cy2eS/dGOw6N9AIiotrPpLlAfxlUXNYLSyaz7HFT81t9XTimAoiL63+jSD3vjsKIDl24HBgZTjMGNbM21n8ntMzE/A5XAlvGculXwTAiQgsQJuEdd1+Rqvwd7TZzGwcRGByjCaQ07sGXLBt2Zz4gO11vQ5nfKCdXFRXqRGo/KxLlfic9vaMFB9At6wHfUR+b+Y+tko4F2bcNwzHmczKq8ODcnnxjOhf6/Zn2/dlirpAliexuobXcTekQ0YqBGxY3ppPXwX54BBg1Mz29pWTvO8cEEGcoqSuGYzGgW2bs19m3Kgq2K21japwb5WAKueG1aqmGr235NDh+D3KhjYOItA5SRa5p3oPGeHT+v11HPu4sXYeaAocu2yw5F6DBb9G0hEpYUZVaISkI/pxNkyMjU3m+xoIbY1+T1cdhcm5iawrXHb8mNyaqGRKouTY4C0LNtMY08PfOMC3SeqceBUM7pPVMM3LhKnNKbKhKxfD6xbJy8+Fxfl7bp1wI4dK94j4ArBM7sofw6HgUgEno6uhOOe8TibkQ3VyECa2b+34LQyfTt2wDdfg+731+LA6RZ0v78WvkvR7LYx1ZTXpiYZlACxab92O3DwoGmblg1ds0RSbdPWraVzbpj898R/+RT6ts5i2h6RMx7sEfRtnYV/7PTKB6vn3FIfaTidsjhaXV36MeT7byARrQoMVIlKQD6mE2fLSPCpa2pekkJta6W9Ej8e+jGef/d5XOW5Clu9W7XXtWYjnwFStlPq1CzpzIzMjgQC2lMaUwWIgLxtaZH9Y1ta5M/J6/a6utByx70Iemvle9XVAffcg6DvqoTjnvE4m3Gha8W1hrlKnoZ+8KB525giEEZ9PbB2bWzatxDAVVflLzNm4IsYdW16yvXiqabxmrnf8s3kvycD19bCOxtFfcQBGwTqIw54Z6MY2Fmj/YSuLuDWW4H2dhng19VlHkO5fUlEREXBqb9EJaIY0w212sQYmZqra2qehnxua/zU5bu23bU8pju23oEzE2cQCAbQ7G7Gnm17sh+DxhRN0y6Cs5lSFz+NtqUlcTzJz0k1XXZiAnjiCV29cDvveAh9rW4gqXJz8nH3/eoMfIe+HzfFeDPQtbTPU03ZTL7QjZ+m7Fn6AiUYjE1ZPnw4b/17LUENxMzaxuQpr/39wAMPAOPjMqvqcMiM+vy8/J3Z+9KMKd/J1Oep58mhQ3If3X8/8LWvyQJgtbXAF79ozXPD5L8ngU/ehNbnXgIc4eXK1Z5IFIGum8wbQz7/BhLRqsH2NESkKVWbmE9t/hRePvuy7vYxVurPCuSnfZCm5DYpZgVI2fR4NNJiwqR2FBmPe6b+jHr6Nya3Uzl3Tu6LTZtktVr2e9QlY9/i668HzpyRbWpcrthU4GxblKTra6rn/Et+fqYWRFrn0sSE/AzV15dGf1AT/57o7qecaj/rHUO+/gYSUVlhH1UiMixdQNfZ3mmp4FO3/n7s/343WgPTsDWuBW6/Hdi5E1ElikAwgAMdBzSflvFCvpCyCSSNBLd6AsRCbUemC9341zh9Wo5TCBmkbt1qfr/HdAGWGY8vAl19i7P5ciSVTOdXpvdKfv7oqJzKrq6h1jpftc61kyfl7fbtsftWSX9QXce8UH8HiGjVYx9VIjIs3VrUjOvCrGjpwqtldB7BukrZbuGZZ4Djx9Oum/WP+fH4Lx7Hi++9iF8P/xovvvciHv/F4xn7weYibfufbNZdGlkvVqjWHHrWoHZ1ye3avDk2ZTN+vWL8a4RCsbYZasscswtYGVkbXOj2HFkW2NJVHM3M9YaZimRleq/k509NyWM+NZW66JbWuRaJyPPk9Gng2DF5u7CwKor96KoDkIfWTkRERjFQJSJN2RRCsrSlC6/O4BqMOxdl30mHHZMvv5C2cNLTx57GqbFTsAkbvNVe2IQNp8ZO4eljT+dlmBl7z2YTSKrB7cgIcOoU8M478oJ8927txxvtG5sNPcFPpmAv/jVcLlmZNBqNtcwxs3iL0Qv3Ql7o5xAU6yqOZmZRqkxfUGR6r+Tnh0KyAnF8P9/kLyi0zjUh5FrbmRkZoM7MyOe43YY2R29PaavJ+GUjq/YSkQUwUCWyoFQXP4W8KLJSS5x4We+DpQsv32wV9p5bA/eiAwGPgHt0Mm1V4V+e/yW8lV5UVVRBQKCqogreSi9eP/+6iVsVoyvDZSCQ9I/50dt4Fvv/uBW9V12Ev3oOqKwEGhuBp57KX4YvEz3BT6ZgLz4AD4dlsDI/LwvjmF3FNcWFu3/stPb5WMgL/RyCYl1fkFGQKwAAIABJREFUSJmZZc/0BUWm90p+vsslA874fr7JX1BonWsLC/J3atuVaFS+TjBxX6ST8UulUsaqvebLtq1YueF+IAMYqBJZTKqLn5fOvFTQiyIrtcRR5XRhGHfh5Zutkn0n36pHd/i6jNukCGXFzwrys74/m96zqSTsr9MXMd3UgL7bGuG/caMsiJMimCnIFyIpAhL/XAC9912D/Xvq0Tv/M/grZxKfFx/sdXXJyq2XL8eCFZcLuHRJblsOU5ZX7IPt61ZcuPttE+jbZdM+Hwt5oZ9DUKz7Cymzsux6vqBI917Jz6+rk0FmXV3610s+1+x2WRDKZpPBvc0mf754UfemGOkpDaC0LtDLsbVTMRV6KYBVcT+QQSymRGQxqYoYvXXhLdy44cb8V6u1sJwq9mZZHOSRHz+CV95/Bd4qL6ocVZiLzGF8bhy7N+3GI7/7SMrnZVuASWsb/WN+nA+ex9Xeq7N/rb/5G6CmBpOOCNyLDnS/v1azIM5LZ17C4689joXoAtbXrEeLpwV2m70gX1L4n/sW+p7+C3gXnfCISgQnLmLcGcHe0avgs6+VD0oueGNSleKEcWgVmzl+FHt7X4MvVLt8/vRuvozpu+9A/Q2/s/zc5fPx8ubCFaPJdh8sFXvyXz6FgWtrMfzJm9B84yfyXyws12qwyc83Wo0WAKqq5Plvt8fuW1yUQevcXOb3HhrC/t9V0PqJO2C79lr5u+PHEX3xRwjMjuDA1E2J4yjF4kTx+1mdEh3f+smq47aiPPydKkncD6SBxZSISkiqjNoHwQ9My7SVqpyyjUamL8ZlPu471I+tV6qgQMHE/AQUKNjq3Yr7rrsv5VvlkvlNznD5x/z42bmfodXTavi1EvZXY+NSv0Q7hl1heV9Shs8/5sfjrz0Ou7Bjg3sDQoshHBs9hsXoYuoskYkG/vUQvItO1Isq2CBQX1mPRUXBl3znsX9rAL3rz8PvupKY1cl1iq1GlkszU7ZzFwYeujPh/Ans6YTn+g8nvNzy+VioolRAdtmvuMyGr2I9ut+04cDf/wrdlzfnf9ZErtnZ5Od/6UvGX2/rVhmYRqPyZ3Xq79atqZ+TlA1qGZ1H8F+/Bxw/Lv898wyCsxNoFp6VmaJSLE6k7ucnnpC9ZsNhZsGyxTW/EvcDGcRAlchiUq0Zu8pzVXkVN8pCzgWe0l0gqwFLUxPwmc8Afj/g9cI3FETPt47j9rlm3LzhZtzefjt6PtaT9mLe8JTAOMlTrs8Hz+PjGz8O3xqf4ddK2F+33w5EIggq82ier9AMZgYGBxCJRtBQ1bC8HremogaBYKAgX4gEZkfgEZXLP4/U2XFscxVGq6NoHQtj2luLvu5b4f/wltiTcplim2IaWuCtn2p/IbLRm3D+tNz4ifTnYyGKUqnvYzQozmPglHbquFWmvx48KL+8sdnkelWbTf588GDq5yTts87gGoxXC0y+/AKiL/4Ik1UC4zU2dF52r9yfpXyBXopBttVwza/E/UAGOYo9ACJK1Nneib63+gAgocfdgzc8iJfPvrzi/j3b9hRzuAXV2d6JQ68ewqW5SwhFQnA5XFhbtRY9H8tx3VT8tLzZWZldGR2VRYfq6uCbAXzfPal7alJgOoBWT2vCfR6XB8dGjqH3jd6M04F9a3zL9+8/sl/ztQLBQMZxJJxLH9qB4GfvwPgvBrDnaFQGM0nT9wLTAayrXYe5yByqK+RFdWVFJS5OX8THN31c17YbkTw92lFXj+DULOpFFQDg3doQbBBYX70Otr87gHoAmJ/EwOBAbL/19MhjByROqdSzli7+AhxYvm35yZsIfihxmn3wnV+h+cirwN5/Xp762Plh7c9qUT6TXV36A+H+fuC112QGsbJSfjlTV2dK4BQ/bbrV04pgKIi+t/rk1PFfnQEeeACYnpbtYUZH5c/f/rZ8ciF7znZ1AU8+aWwK8tCQ/EJjiW+2CnsvrMdA9QgCVRE0Cw/2nHPDNyvP34T92da2cspjqVygJ203gNIJsq0il79T5YT7gQxiRpXIYlIVMbpty22WK25UDEIIQMHyPyFE7i8aH7CEw3Ldms0mq8kCGS/KkjNIDuFYkWl7b/w9nBk/szwdeHBiEPte2Icv/McX0hYsyiWLHH8uHRs5hrfq5jC159MY+NZ/g//7/7TiorzF3YJWdytmF2YxuzALRVEwMTsBh81herVnrenRF27ZjjNVc5hU5hCFgosV81hUoth20/+1/LwVU71zmWKbIsvVeXwmscDQ27/EeP9z6Dy9kJB59f3qTPE+k9lmJtUvZdSeswsLwLlzsg+pCYFT2tkEDz8MjI/LANlul7fj48AXvpD/Aita+8toxlsjG+S7FEV3+DocmLoJ3SdrYkEqkLg/S7k4EbNguSvkUgAr434gg1hMiYiKTm/hoZyKKaXT1iYvkIUATp+OBauRCHDttWmLPWgV3nlv/D0oioIta7Ys3/fy4Mu4rklWGB65MoJXP3gVNsgL+Rs23IDxuXHNIEezsE+Kx6biH/Oj79n/Bu+rb8IzMoFgUwPGP3YT9t79twmvob7XYnQR56fPY/TKKBw2B/7i1r/AbVtuy37/akh1LOdOvIN1rxzF8NwIzqx3ofX6j8O36/aEx5hWQCxNYQ//9/8JA4MDGJ4eRnPf/0Ln6QX4FO+KxxW8AEh/vwz4TpyQX65s2ABUVOgvzKNucyQiA1T1ix67HVi/PueLRnUGgE3EvgePKlEEggEc+PTj2gWMFhbk2tB8FVjp70/M5DocsjjQt79tvIhTqoJIb7wBPPZYrPp0ff3KytNGi0jFFW4qagGjUiwERUQlg8WUiMiyjBQeMrN1S4L4jEFTk7yYjkTkhVmGzIdWBulq79Vo8bQkZNraG9pxtfdqAMC7l99FdUU1GqobMBWaSrvu1Iw2QQM/+Dq8/zGA+vEZ2GpqUT8+A+9/DGDgB1/XfK/2hnZsadiCu3fcjcO/f9j0IBVIfSwjV7ej+3uncOC5STx64OewX73FWC/fpMyZ/7lv4SuvfAVd/9yFrn/uwiM/fiR2bqXJcvnW+NB9SzcOdBxA9y/C8EUbEt+nGFMf1YDhzJlYRvKDD2Lnqp41g2oWua4O2LhRPk/tI2pC4JHVDIBoNL/rN1Nlch9+2NjrpMoGAbIncWOjDFLn52XLpPvvT9yfRjK4VmrjwSwYERUJ16gSUVHFB3oAlm8T1iEuUS+CE9YOmlFQKn7djMcDrFsnLzRrajTXcsZLtR51OjSdkPXrfaN3eeyT85Oor6zH/ML88rakW3cav2Y1G4Gf/hCtilMGJQDgdMITVhD46Q+Bzz2R23tlmfXRcyzVwHlgcACBYADN7mbs2bYn9fjiMz9eL/zTZ/H4s3+GUzdvhrdlCxSh4JX3X8Hw9LAsiKVegGfKchldX5ivTJg6RV3NCgIy6BoZAXw+uQ2Z3jt+W+rq5D81e5nLGJfet/PyKfTdYgc+KisiJ6zb3bpVZoLVvqVqpd2aGrk/87V+8/Rp+X5qJle9PX3a+GtprQXu6JDHpaFBftEFyH36yiuyInE2UqyfxqFDxQkQjayBJiIyCTOqRFRURrKkya1bdGXY9EjOGPh8wHPPARcvwv/9f0Jv41ntCqbQn0GKH7un0oOJuQnMLMxgW+O2lM8xS8vwNILV9oT7gtV2NA9fye2Fc8j6dLZ34szYGfSf6ce/vftv6D/TjzNjZ1Ycy4TM5i3d6YPopOqkAxsXMVodxZrBi6h2VqOmogbeKi8uzV2KZa/1ZLmMrC/MZyZMzYa6XHK67Pw8EArJ1iHnzsmA9c47gddfl8GY1nv39Mhz/ORJ4NgxeTsxkdtayaRWN3t/44D72R8g8OZPEmcApKq0+5d/WVrrN+Oz9tdfD/z858B778nAd2pKPibXjHApVwkmIjIJA1UiKiojUwXNmAabkkbAomdast7gOX7sDa4GLCqLuHbdtVhbs9a8gDuFTtsWjIt5TDoiiELBpCOCcTGPTtvVmo9P214kXo5tK0wvjJV0cR+oDGO+wobK6RlgeBgYGEDVD3+E0Ks/xfBbP9X1kv4xv/yi4q8+jN6bovBHRtJPfcxnKw91irrbLQM9tcaEosgZAOra6mg0/ZRgdT+rzxdCrrHMtm1M0jb7FC+6zzbiwL+MJH65oFba/chHgNZWefvkkzLrmM+ppdn0TE0l/osIu10G+pGI/J2ZhalYwIiIiMWUiKi4zCgWlC96izepxaCGp4fR7G5OWQwqXjbPyVp/P/x//XkMtC5g2CPQHFTQeb4Cvse+uSIYMHQ84otQqRRFBhuDg2mHZEZhrBVFuL78HfiGgsvTJHs3XcKLDWOwCTuqQlHAbsNshQ1KNILbA5Xo/tPvpg2Gsjo3c9gnGalB0sWLMihdXJSvHT+VtrJS/hyNyiJLPl/ie2sVkBoZkYHu5s0y0B8dlT/X1QE7dmSeutzWJoO20VGZ4XW55PT5aDT3bTZDfz/w4INAMCiDyYoKOcX/ySeNB8Px+08tvBaNxr4UAMwpTMUCRkS0SqQrpsQ1qkRUVIbXIRZQqvWnyWtJs1lDmuu6U0O6uuDDN+E7dAg4cVYGJI9pBx9G1gzn0htS775NRbNf512bsLf3NfhmINvMvBvC21vmcMobhTdig+KoxIRTYOuMC52X3RnX+xnaF6p89stUp6h/5jMyMK2qkmsiz52TwdH8fCxrqE4NfvddYMuW2Gto9cScnJSBVk2NzAaOjsr7p6bkNOI775QB68GD2vvL45FrTx0O+W9hAXj/ffkcK8imZ2oq8fsvFJL7Xc1iO53yPjMKU+ldP01EVMYYqBJR0RU0aDMgb8WbikFnMRRDAWQOzdtz3beaQeTOXRh4aC183z0JnDgB39QU/gJr8XTTJbzeokBZmMXuQD3uG1sPX7Qy43q/rILpfDe07+oCbr0V8PtlIHnunMysqm1RIpHYVFQhZNA4MhLrG6oVSIdCMhMLyMeq2eD5eXm/3S4rDe/blzoAE0Jmd9V/ZvQ3NpNZxYDi95/LJY+tEPJYb91qvDBVuuJXLGBERKsc16gSEaWQt+JNFmaovUgObSv07Nt0a2VTFuHa6JVrjHfsADZvhq+qFV8+WoMXvu9E/7OVeOTlRfhmq3RlObNqtVKIVh67d8vpv2pGD4hVz7XbY0FiZSWwaZOsRquuU9UqDGW3y76fgHzN+GJH6r90LXCCwVirm8VFebtxo+xbmo2kFkNFacmSSvz+U6c3RyLyv40WgcpX4a0M+0/3GnQioiLjGlUiojQKupbUAgq5Zjjdvs00joxrXOPXiqqZR3Xt5tVX61rvV5B9oZFR8394S+La2+RzrqMjllFV14Sq2dQrV2SAun69XGMKrFwjq76nOqV0927ZB9TpBM6fl/smHJavqfYddTpXrneNH09yllbNLB45Ynx/WH1tZvz+c7vlfdPTxqfnmrnf4seWZv9ZuSYAEa1O6daoMlAlIioDKwoL5RBQ5z0419FnNFMgmvGCOzkImJqSlX+jUTl1VmdAYfq+iN92j0euB62vXw4q/K4r6Ou+Fd6du1IHEukKNm3enF3wo47rxAm5r+KvDRRFZkgdjuXXSTjfzk2g8+vPwxeqzT24NBq85atnbSHko/BWhv1nRhEzIiIzpQtUOfWXiKjQTJ7a+NKZl7DvhX149sSzeG/iPQxODK5oo2OEod6lRumc7pipv27GVkXJU1wdDpllfO65Ff1S002FNHVfJG/7mTOyum4kstzKZqB1Ad5X30R9ZT1swob6ynp4q7yxvq9A+tYlRnq+xlPbM128KPfRNdfITKrdDlx1ldx/S6+zom2TbxP6um+Fv82T+3RnI/1D9ZxLVp5GnI8WNBn2n5G+1Xlj5WNCRJbCQJWIqJBMXpfmH/Pj8dceh13Ysb52PUKREI6NHsNidDExuLEKnX1G9awPTRtE6lwrqqdXbt62PRKRgeDIyPJDAh4Bz8hEwtNWBBLpglEz1sh2dQHvvAM8/7zsdRqNJrxOfCGr5WB65y4MfOVzCX2IDVGDl0AAePtt+e/06fQ9STOdS1qftQcfBK6/3hpBUrZfKqSTIfjNat21mfK1LpeIyhIDVSKiQtIZqOk1MDiAhegCGqobIIRAdUU1qiuqcX76fGGzJHrpzJiZUshKzRKmCZ40g67kDKZOGYvUJG+7yyVvQ6Hlu1qCCoJNDQlPWxFIZApGdWy3Lilex/SsnBq8+P0yKI5GZTZ3bk6eF5OTMnhLzsSdOJH+XNL6YuDyZZnJtkKQlI/CWxmC36IXiDP57x8RlTcGqkREhWRkaqMOgekA1tesx/zC/PJ9VY4qjF4ZtWYbHZ3THTNO7TWJWUGXrsxs8rY3NcmgzOFYDio6z1dg/GM3ZQ4kzApGs9BybgLBbzwO/M3fAF/9KnD8eG5ZOTV4mZoCKipkAC9ErIjTunXyccmZuPier6r4cyn5szYyIjPYcVOtix4kmX0c44PfQEBO5b5yRW5jf3/BPlcpmfz3j4jKG/uoEhEVklYfyxzWpbW4W5an+wJAeDGMk5dPYnJ+EgvRBYxcGcF9191nnYqeBvqMFqK/rlm9cjX7ui7dn7BuNn7bHQ4ZdDU1LRdC8vX0YK9a9TcYQLO7GXu27bHO8evvR+fXn0ffNbNAXSU8U5MIPvvPGP+DTuy5+2+ze82hIbkfQiG5TwBZuXhxEdi2Te6b+EwcIG8bG2WGtLZW+1xK/qyFQjJAtdnktOJQSL7mxMTKMZUyNdjdt0+2JqqujmWPDx+Gr6ureOeTyX//iKi8MaNKRFRIJq9L62zvhN1mx7XrrkUoEsKbw29ifG4cW71bsaZ6DV55/xUcevWQdXolFqLPqAFmTYXUlZnV2vZvf1uuB43LqOW1mFWuDh2CL1SLvRfWw73oQMAj4Fac2Pvv72c/TjXT7HLJDDMgb12uWBCjlYlbt0624El1LmkV1FpYkBnVcFhmV8NhmZkttzWSVp1im491uURUthioEhEVksmBmjqVr72hHYvKIhprGrGreRfave2oqaiBt8qLS3OXrFVYqYjTVpPpmgqpo0ppxiI16mt84Qvy5yeeKO62Z1t5dSlg9M1Wofv9tThwugXdF1rge/dS9mNRg5e6OplFjURkEFNXFwtiUk0Z37Ej9bmU/FnbskUGpzabvFVb8DQ2Fj+AM5tVp9ha7IsqIrI29lElIioT+4/sx6+Hfw1vtRcCsjejoiiYmJ/AzRtuxoGOA+a+oVYPS6A0+lrq7b+pFvpxOtP2CE3b1/VXZ3S9RsHo3CZNRvuc6hnLoUPAb38rx6ByuWQQqh6XXMYcb/36WBbP5ZLTrj2e3HqXWtFq6kdLRCWNfVSJiFaBFncLKh2VCYWV5iJzcDlc5hdWStX644EHrN96QmeLDP+YH73//BD23zyF3h2z8NfMp5xCmTYza7VpmLmMJ3nq5siIzNL99rfG273EH4fWVhlEer3A00/LIkDxGVKzMnE7dsj3uvZaYOtWmbXNdo2klfuBGpliy5YxRGRRzKgSEZUJ/5gfj//icZwaOwVvpReKUDAxN4Gt3q3o+ViPuWsdtTI2J0/K2+3bY/flkm0zSm9WSEe2aTlD+vVvwuOsRdCxiHHnIvaeWwPfTKWxDFxbmwwAhIjdpyjFy+LlOh51P584Idd3NjbK9aJGs5xmZ2f1MCsza9br5JN6nM6elYF4Dp8HIqJ8SZdRZaBKRJblH/PL6qfTAbS4W9DZ3mmtwjIW5B/z4+ljT+P1869DgYKPtHwkP1V/tYKd3/xG/nzttbH7ChWQGQkcdARqvW/0Yjo8jfp/fFIGY04nJh0RuBcd6P5VVLb8qK/XN03SaoGAWePJ9XWKFcDrDeBSPW9oSPZ2ra2VU4dVpRrcWe2LFCJaVTj1l4hKjq6+lLSCb40PX979ZbzwRy+g/4/68cjvPpKf4F6ruE1FRay9CAB/9Rx6NwSwf3cUvW/05vfYGZnOqqOX63IV39tvX64S64nYMbw4Iael1tbqnyZptUqnZo0n14I9S8fBXz2H3k2XsP+aAHo3BODfttbYONLRmp6bTTGv5OmxMzNy2vPUVOwxRrbdStOGdfY2JiIqNAaqRGRJ8X0pbcKG+sp6eKu81qpeu8r4x/zofaMX+4/sR+8fb4ffdSUx2PF4ALcbmJmBv3oOfRsuYlqE0fqJO/L/RYORoElHoLZcxXfnTuCee4C6OgTDV9A8HpFrKZua9K/vtFqlU7PGk2uA09MDv+uKPE/sEbROKZgWYfTdtcmc88TMtZfJX4RUVsrbkZHYY/Rue6pxPfpocYJXo+tZ043RSgE4EZU8BqpEZEm6+lKSOXRcXK7IcPs2oa/7VvjbPLFg58knZV/QDRswUD0Cr6sB9Xf/EWzXXpv/LxrUoGlqCjh9Gjh2DHj3XRk4J9MRqCX0V/3QDkx+4UGMP/R5dE42yPWY8fRk0izUkkf3eDKdF7lmZru6MPDQnfI8mQrBVleP+rv/CN6du8w5T8wsYpX8RYg65Xduzvi2a40rHAYee6w4BY30fnGRKfDX83sGsURkANeoEpElLa8RrKxfvm9yfhJupxvdt3QXcWRlRufaTqPHY/+R/Wj1tMImYt+HRpUoAsGA+W1y1O148EHg8mXZIxMAolF5wfztb2cVGKprpIenh9HsbpZrpD/7f2del1kOrT6Sz4vRUblv6+pWto3JZr3nkryeJ+ray2BQZj5DoViAePGisdfSWo87MiLXKjc0GNt2rTWhp07J8V13Xey+fK15zfb8zLQmOd3ve3qsX3yKiIqCa1SJqOQkZLSUKCbnJzE+N47O9s5iD6286Mw6Gc1wL0+djRMMBc1vk6Pq6pKZTqdTZricTmDTJhlEZNkGxrfGh+5bunGg4wC6b+mWa30zZRHLpdVH/HkRDMpANRqV2xu/TTlmivN6nrS1yXGfOyePkd0ub6emjB8PrePudMpWOka3XWvK9Py87Osaz8iaV71yOT8zTa9P93urtWiiBAnLOvJdT4DIAAaqRGRJaftSknl0ru00GlAU5YuGYBDYti2xR6bZF/uZpknquSAvhSmQ8efFyIjcFodDBmsmBhl5PU96emQWGJBBqjqDrLHR+NjNXGesFfQ6HLKKdLx89HfNJWDMtCY53e9zLbxFecPChWRlDFSJyLI0M1pkLp0FcYwGFHn9oiHVhXihqpcuZRH9b/wIvX//Wex3vRrLQmS6IM8141qoIDd+X4ZCgM0mM6pq1s+kICOv50lXl/yywumUlZsrKoCNG2XmPZuxm7XOWCvo/eu/luPMtRJzfz/wwAPA668Dw8Py9oEHYudJLgFjptkE6X7PysKWxcKFZGVco0pEtJoZ6D+quWaz0F8epBsvkL91cEnr+vz77kWf5wy8VV54XB4EQ0GMz41j71d/DN9QMLt1fJnWIhrpFZur+Pc6f16+DyADvbq60ukZWowettmuAc1xvS8A4PrrgZMn5RcL6pcL0SiwfTvwzju5749MY0z1+0Keu/HjKOV14gVS8HoCREnSrVFloEpEtNplcYGsBq2B6QBa3C2FC1ozXWibcbGfTOMiu3fzZUzffQfqb/id5YdNzk/C7X8f3Qf+T+oLcq1COooiM2uDg7ltu9nUfXnihFzX2dgos5H5CjLyEVwUI0AqZtGgqip5PqkFxQBgcVGeb3NzxR1fPj6bqd5H3caFBfmZCYdlEbCDBxmwJmHhQio2BqpERGQadU3TimxihimbpgS3uQR62dIIEPe3n0VrxRrY/uzPl+9bzkKEPpr6gjyXYDPbbTcjAMx3kGF2ABW/zZ6lImDxlX8/9KH8BErFyODGyxSoAoULGItFPQaRiCykpX5e7HbZA5mVhhM+H/7t69B31yZ4d+4y9PecyCwMVImIyDTZfAOfbXC7QjECAY0AsXfjKKbDV1D/lYPL9+nKQmQbkPX3A/feK9uhVFXJPp56puAWO8Onl5nHVWubJybk8auvz+9+KMYXKfGuv15mwB2O2NTfSERmE995J//vbwXqMfD7Y9WeAbkf2ttLY8p6Pml8PvyuKxh46E4Mb/QWb1kHrVpsT0NERKYx2qoGMLFgR6aCLoD5BYc0CsF0fuDAeFOt8Wq12VSPVS8sa2tl8BEKAe+/LzOEmQrulEpbEDOrwmpt8/S0rAqd7/2Q76JBWud2/H0A4HbL82RhQd42Nsopr6uFegzUImBArBAYKw1rfj58oVp0f/ckCxeS5TBQJSIiQ7LpfZlNcKspU6CXjz6mGsGxb1xg76e/lF21WqPVY9ULy6YmWcxIrbx75UrmILdU2oKYGeBpbXMkIgO3ePnYD3q+SMmW1rn9wAPAgw/G7guH5bmyZQvQ2gp85CPAk09aK3ueb+oxcDhixaSiUfn5YaXh0vmbQATAUewBEBFRaels70TfW30AkDCNd8+2PSmfowa38dOFMwW3KXV1pb7wjs8WALHbQ4dybyeStK7P19WFguQdhoZkEALI6b51dbHppJm2qa0tcUrt1JRsWxKNyixcLusTzSx+1NMjgzAgcWpuNgFe8jYDMmhJlq/WRRrnSq7rbP3b12HAeR6Bm0NoWaxB5yUbfKJGrsEEgJYWeatu85o1q2eqbzL1GDz8sJwG7XTK/aP2ATbjS4NSpvX5YABPFsWMKhERGZJN70ujfVizlq9sgVk9NLORS7YxPsM3OSmnDIfD8sI9l2yz2ZnrbKZEp6KV1XS7AY8HfjGO3o2j2N9+Fr2bL8O/797sxptpW3I9V+L2r/+qavS5z2B67AJapwWm7RH0bRyDv3qucJniUtPVJQP155+XWeVoNLdzqpzkM+tPZDIWUyIiooIoSB/WYlddzYdcCyKpmbnXXpNr9pqbZVYWyH7fGNzPBW9npFHZ1j8XQN8PH4V35Ao8dWsR7PgYxjets2Z107j927vpEqbtEdSfvyx/t349Jh0RuBcd6P4/S/dt3x57bjHPd/YvLQ3lXvmZSgqr/hL2jne3AAAeYElEQVQR0epQKlVu4+m5uDfjwtLMirQGXsu0is/xsgiIcuoXWegALG7/7r8mgNa5Ctjm5oGxMWDdOkSdFQjYZ3HgZ47CVDPWoxQ/e0RUdKz6S0REq4OZU0gLQe8UWjOmk5pZsEjva/X3Y+BP/wDer38T9f/4JGy/PZF9xee410y5z9JUfM66oJfeY2Rmtem4/dsy70TQsSjbrGzYANTVIRi+gmbnGuDb35bFkqxwvpdKhWkiKhnMqBIRERVLIacqm5nx0vNaS4/Zf/MUWiPVsIUX5JrKe+5B9EM7EAgGcCD0UeOZylT7zOmUlZBTjCnrjKqeY2R2NjHu9fxrbejbcBHeWQWeP7wPQd9VuWek86HYPWSJqCQxo0pERGRFhWwVYWa2Wc9rLWXYWhZrZEbQ6ZSVV198UVZ8PjeeXUGmVPvs9Om0Gb2sC3ppvd/CAvDznwNVVfLf3XfLwNRINjFdBjZu//rOz2Hv9Ba4//AeBDbWaxcvM7t3cDby3UOWiFYdZlSJiIiKpRyLP6mWMmz+mnn0bRyDN2yHJ2JHMHwF4w99Hnu/+mP4hoLGtz3VPjt7FtixI21GL6uCXsnvNzUlg9fFxVhP21BIFqpqb48VqkqXTSx0drsQrDIOIiopzKgSERFZUTm3iljKsPlmq7D33Bq4Fx0I2GfhrlsrM4InR7PLJqfaZ1u3Zszo+db40H1LNw50HED3Ld36ps4mv9/wsAxSKyrkulG7XQbHigKMjKR87wRmrue0ytrQUlsfTkSWl1OgKoTwCiFeEkL4l24bUjxuUQjx9tK/53N5TyIiorJh9OLeClM89YoL8Hwzleg+UY0Db9ah+4++LgPEbKeKptpnBw/K9xsZAU6dkn00z54Fdu/ObTuS3y8alcFpRUXsMRUVMlCdm9P3hYOZU74LOX08k2L2GyaispPT1F8hxH8HMK4oykEhxMMAGhRF+SuNx11RFKXWyGtz6i8REVGc5KmVo6PA5ctyqumOHdbshZiurU4+poo++ijw2GOxabn19fL1zczsdXQAr78uA1bb0vf9i4vytqoKaGjI3ELIzCnf5Tx9fJUreP9hoiLI59TfuwA8tfTfTwH4Tzm+HhEREWmJn+IZDMpANRqVQYneQkTJ8p2hTZdh6+oC7r8fuHhRZj8vXpQ/xz/G6PheeUUGidddB1xzDdDUZP402J4e+ZqhkAyu5+ZkcaX6euDpp/VlE82c8l3O08dXMbX/8HR4Gq2eVkyHp9H3Vh/8Y/5iD42oYHINVJsURbkAAEu361I8rlIIcVQI8UshRMpgVgjx+aXHHb106VKOQyMiIioj8VM8R0bkekSHI1Zt1mhAprc/aL709wNPPQWsXw9cf728feqp2PtnM75CTYOtrJT7G5DBocMBfOEL+rO2ha7ATCVnYHAA3iov6ivrYRO23PsPE5WgjIGqEGJACHFc499dBt5n41JK914AXxdCXK31IEVRvqkoyi5FUXatXbvWwMsTERFZRL6ylPFrOtUqs9ForPKs0YCs2EV4Mr3/ww/LLOvgIOD3yx6smcZXiBYphw7J7OnOncDNN8t/7e0ym2uEmes5k18LKJ21zIVUQmu8A9MBeFyehPs8Lg+Gp4eLNCKiwssYqCqK0qkoyk6Nf/8OYEQIsQEAlm5HU7zG8NLtIIBXANxo2hYQERHpUYiL1HxmKeOneDqdcl1kNCqntwLGA7JiF+FJ9/79/cCJE3IbHQ45tfbcOXmbbnyFmAZb7P2WSbEz5VZVYvulxd2CYCiYcF8wFESzu7lIIyIqvFyn/j4P4P6l/74fwL8nP0AI0SCEcC39dyOAjwE4keP7EhHRapVNwFmoi9R8Zinjp3jW1MiM6rp1gMeTXUBWiOxjtu+v7keVzSb/XbiQfnyFmAZb7P2WSbEz5Rbl/4dH0Lt9BvtvmkTv5svwr7Wl3i8WyLx2tndifG4ck/OTiCpRTM5PYnxuHJ3tnQUfC1Gx5BqoHgRwmxDCD+C2pZ8hhNglhPjW0mO2AzgqhHgHwI8BHFQUhYEqEREZl23AWaiL93xn29QpnhcvAs89B/h82QdkhS7Ck3zxv3u3HPvJk8CxY/J2YkK+/9CQ3CZFkVlVRZH/Mo1PrTI8NJS58m62rF68KJtz0AKBWVZ0jts/5kdf1buYrrajda4C0/YI+jaOyWA1eb9YJPPqW+PD3hv3wu10IxAMwO10y/7DrPpLq0hO7Wnyie1piIhohWxbcbS1yYtOIWL3KYoMjAYHize++MCqra3wLWbStY8x+32SW9FMTgLz8zLIW1iQvUg9HuDJJ+WYLlyQ61JHRuSaXIcD2LJFVgjW+x65trtJtz0PPwycPi1/3rpV9nG1QgGjbM7BQu03MxkYd+8bvZj+h/+B+vGZ5Uz9pCMC9+wiumd3JO4XtvshKqh8tqchIiIqnGwzloWarmkk22aFzI2ZBX3S0cpoB4Ny32zfLtvJbN8uixQdOhTbjw6HzBq3t8uqwAcPpn6PbIov5eLKFXn+7Nghx2qV9Y5GM75FmirsH/Oj941e7D+yH71v9Bpvu2Jg3IHpADwdXfKcCIcBAJ7ZRQy7Qiv3i9XXIBOtIgxUiYiodGQbcBZquqaRNZKraS2h1sX/woIMHOKpAYHRtabZFl/KlpWPndF9V4TAzJQeoQbG3eJuQdB3FXDPPUBdHTAzg6C3Fs133Ltyv1h9DTLRKuIo9gCIiIh06+mRmSsgcbpfpoBTvXgvxDTXri59rzs0JDOp8co1c9PWtnI6ZUXFysfFBwR69yMQCxwXF+XPtqXv4S9cAD7yEUND9Y/5MTA4gMB0AC3uFnS2d65cF2j1Y2dk32kdm3SBmQnT1eN7hAJYvh0YHNC/BtPAuDvbO9H3Vh+wpRWeDz2EYCiI8blx7Llx78rXzfZvDBGZjhlVIiIqHblUdS3UNFe9VlPmRiuj7fEAbrc5We5siy8l0Z3pK6djV4Tp6qb0CDUwbkOFiQpROZqIdGExJSIiomIo1SI22Xr0UeBrXwOmp2WA+sUvArfcYk6WWy2AY6T4kobeN3oxHZ5ezvABwOT8JNxON7pv6Y49sNyOnd6iWiYVGtK9n80aNxFZFospERERWY0VMjeFakvS3w889ZQsiHT99fL2qafk78zIcmdTfEmD7kyfFY6dyoxjqHe2gUnrWU3rEWq1WRJEZCpmVImIiFajQmYF89nyQ82q/fa3cvwul6zEm0V2zbRMX6EUOrNr4nFU1wIPTw+j2d2svRaYiMpeuowqA1UiIqJcFbsfajYK2S8yX31sTQ7U1DWq3iovPC7PctGdlOsZi63QPT/LbcozERUdp/4SERHlixX6oWajkG1J8lV8yOQ2MYaK7lhBoVvLWGnKMxGVPbanISIiykV8sATEbg8dsvYFvNG2JLnIV8uPPLSJ8a3xWTcwTVbIY6gy0vpGj1KcjUBEBcGMKhERUS4KndUyi5G2JLnKVyYu20xtoYpI5Vshj2G20u3rUp2NQEQFwUCViIgoF6XaU7PQ0zjzUaE1m0At2+DIisGt1afiZtrXJk/dJqLywmJKRES0epkx7ZAFZorLaC/NbAoQ8RhnJ9O+zleRLSIqGSymRERElMysaYdmZbWsmLErBUYztdlM1WbmLzuZ9nWpzkYgooJgoEpERKuTmcFHrtNauVavcLIJjkp1HbJRZn9Zkmlfl8IaWyIqGgaqRES0Olkp+GDGrnCyCY5WQ+YvH1+WZNrXVl9jS0RFxUCViIhWJysFH1YKmstduuAoVUZxNWT+8vFliZ5ANB9FtoioLLCYEhERrU5WKpCTTYEfMlem88Fo0aZSw8JGRFQELKZERESUzErTDldDxs7qMmUUyz3zZ6UZBkREYEaViIjIGso9Y2c1ya2JTpwAWloKmlH0j/kxMDiAwHQALe4WdLZ3wrfGl5f3yshKMwyIaNVIl1FloEpERESri1ZQdvYs0NgINDXFHpfH6df+MT/63uqDt8oLj8uDYCiI8blx7L1xb3GDVX5ZQkQFlC5QdRR6MERERERFFT/NF5C3jY3A5ctAbW1iRjFP068HBgfgrfKivrIeAJZvBwYHiheodnUxMCUiy+AaVSIiIioMs/t0ZkuryvK6dUBdXcHWLAemA/C4PAn3eVweDE8P5+X9iIhKDTOqRERElH/x023j+3QWYw1kW9vKKsuzs8COHQWrstzibkEwFFzOpAJAMBREs7u5IO9P1mepNcxERcCMKhEREeVfPvp0ZmLhvqid7Z0YnxvH5PwkokoUk2//EuNPHkbn3v+3uNlmsgR1DfN0eBqtnlZMh6fR91Yf/GP+Yg+NqGAYqBIR0epglWmnq5XWdNvqalm4Jxepjquawb1wITGD299vidZEvjU+7L1xL9xONwJv/gTuZ3+Avb9xwFexPnGsueJ5X5Li1zDbhA31lfXwVnkxMDhQ7KERFQyr/hIRUflj643i6+hYOd02l6q6/f3Aww/LtjJOp3ydiorYcT10yNz3yyez942K533J2n9kP1o9rbCJWE4pqkQRCAZwoONAEUdGZK50VX+ZUSUiovJXjGmnlMjM6bZqAHbmDGC3A4uLwAcfAJFI7LjmK4ObD/kaazHPe2Zyc6KuYY7HNcy02jBQJSKi8ldKQYuq3C70zZxuqwZgkYgMVO12GYiNjMSOa1ubzCDGm52V/UGtJl9jLdZ5n27aNemyYg3z/CTG58bR2d5Z7KERFQwDVSIiKn+lFLQA5Xuh39Ulp7IODsrbbKefqgGYywVEo/I+mw0IhWLH1QIFk3TL11iLdd5zBkPOEtYwBwNwO93Ye+NeVv2lVYWBKhERlb9SCloAXuhnogZgTU0yUFX/ORyx42qBgkm65WusxTrvS3EGgwX51vjQfUs3DnQcQPct3QxSadVhMSUiIlod+vtloHf2bCzjZsWgBZCBmNcrg1SVosggZnCweOOyivgiQQsLMuMcDss+qAcPWve4FkO25736vKEheT4a+bzkqzgUEaVVir130xVTYqBKRERkNbzQz6yUvngoNblWC2a1YaKCU3vvequ88Lg8CIaCGJ8bt/yUcVb9JSIiKiWlNlW5GMxa70or5Tr1vJSmXROViXLsvctAlYiIykepVspNHjfAC30qHjPWmPKLBKKCCkwH4HF5Eu7zuDwYnh4u0ohyx0CViIjKQ6lWyk01boAX+lQcpVYlm4jKsvcuA1UiIioPpVopt1THTeWLU8+JSk459t5loEpEROWhVFtilOq4qXxxjSlRySnH3ruOYg+AiIjIFG1tKyvllsJ0xVIdN5W3ri4GpkQlxrfGV9KBaTJmVImIqDyU6nTFUh03madUi4AREeURA1UiIioPpTpdsVTHTeYo1SJgRER5JhRFKfYYNO3atUs5evRosYdBRESrXX+/LGw0NCQzXj09DCLJPB0dK6d+z8zILyyOHCneuIiICkAI8WtFUXZp/Y4ZVSIiolSY7aJ8YzEtIiJNDFSJiIhSYesYyjf2LCUi0sRAlYiIKBVmuyjfWEyLiEgTA1UiIqJUmO2ifGMxLSIiTeyjSkRElEpPj1yTCshM6uwss11kPvYsJSJagRlVIiKiVJjtIiIiKgpmVImIiNJhtouIiKjgmFElIiIiIiIiS2GgSkRERGQl/f1AR4cs5tXRwb69RLQqMVAlIiIisor+flnA68IFwOuVt/v2MVglolWHgSoRERGRVRw6BDidQE0NIIS8dTrl/UREqwgDVSIiIiKrGBqSrZDiVVcDZ88WZThERMXCQJWIiIjIKtraZL/eeLOzwObNRRkOEVGxMFAlIiIisoqeHiAcBmZmAEWRt+GwvJ+IaBVhoEpERERkFV1dwOHDwIYNwMSEvD18mL18iWjVcRR7AEREREQUp6uLgSkRrXrMqBIREREREZGlMFAlIiIiyrf+fqCjQxZL6uhgX1QiogwYqBIRERHlU38/sG8fcOEC4PXK2337GKwSEaXBQJWIiIgonw4dApxOoKYGEELehsPAvfcyw0pElAIDVSIiIqJ8GhoCqqtjP09NAaOjwJUrzLASEaXAQJWIiIgon9ragNnZ2M8jI/K2qiqWYXU6ZeaViIgAMFAlIiIiyq+eHjnVd2YGUBRgfl7eNjXFHlNdDZw9W7QhEhFZDQNVIiIionzq6gIOHwY2bAAmJmQGtakJqKuLPWZ2Fti8uWhDJCKyGgaqRERERPnW1QUcOQIMDgJPPy2n+qoZ1pkZmXHt6Sn2KMsLWwIRlTQGqkRERESFlJxh3bBB/tzVJX/PACt3bAlEVPKEoijFHoOmXbt2KUePHi32MIiIiIgKRw2wnE65bnV2VmZb4wNZyqyjQwanNTWx+2Zm5JcCR44Ub1xElEAI8WtFUXZp/Y4ZVSIiIiKr0Oq5yorAxiW3BAJYsIqoxDBQJSIiIrIKBljmSG4JBLBgFVGJYaBKREREZBUMsMyR3BKIBauISg4DVSIiIiKrYIBljkwFq4jI8lhMiYiIiMhK+vvlmtSzZ2UmtaeHARYRlaV0xZQchR4MEREREaXR1cXAlIhWPU79JSIiIiIiIkthoEpERERERESWwkCViIiIiIiILIWBKhEREREREVkKA1UiIiIiIqvq7wc6OmSP3Y4O+TPRKsBAlYiIiIjIivr7gX37gAsXAK9X3u7bx2CVVoWcAlUhxGeFEL8VQkSFEJr9b5Ye93tCiFNCiDNCiIdzeU8iIiIiolXh0CHA6QRqagAh5K3TKe8nKnO5ZlSPA/gMgJ+meoAQwg7gCQBdAHYAuEcIsSPH9yUiIiIiKm9DQ0B1deJ91dXA2bNFGQ5RIeUUqCqKclJRlFMZHvZhAGcURRlUFCUM4F8A3JXL+xIRERERlb22NmB2NvG+2Vlg8+aiDIeokAqxRrUFwAdxP59fum8FIcTnhRBHhRBHL126VIChERERERFZVE8PEA4DMzOAosjbcFjeT1TmMgaqQogBIcRxjX96s6JC4z5F64GKonxTUZRdiqLsWrt2rc6XJyIiIiIqQ11dwOHDwIYNwMSEvD18WN5PVOYcmR6gKEpnju9xHsBVcT+3AhjO8TWJiIiIiMpfVxcDU1qVCjH19w0APiFEmxDCCeA/A3i+AO9LREREREREJSjX9jR7hBDnAdwK4IdCiB8t3d8shHgBABRFiQDYB+BHAE4C+F+Kovw2t2ETERERERFRuco49TcdRVH+N4D/rXH/MIDfj/v5BQAv5PJeREREREREtDoUYuovERERERERkW4MVImIiIiIiMhSGKgSERERERGRpTBQJSIiIiIiIkthoEpERERERESWwkCViIiIiIiILIWBKhEREREREVkKA1UiIiIiIiKyFAaqREREREREZCkMVImIiIiIiMhSGKgSERERERGRpTBQJSIiIiIiIkthoEpERERERESWwkCViIiIiIiILIWBKhEREREREVkKA1UiIiKictTfD3R0AG1t8ra/v9gjIiLSjYEqERERUbnp7wf27QMuXAC8Xnm7bx+DVSIqGQxUiYiIiMrNoUOA0wnU1ABCyFunU95PRFQCGKgSERERlZuhIaC6OvG+6mrg7NmiDIeIyCgGqkRERETlpq0NmJ1NvG92Fti8uSjDISIyioEqERERUbnp6QHCYWBmBlAUeRsOy/uJiEoAA1UiIiKictPVBRw+DGzYAExMyNvDh+X9REQlwFHsARARERFRHnR1MTAlopLFjCoRERERERFZCgNVIiIiIiIishQGqkRERERERGQpDFSJiIiIiIjIUhioEhERERERkaUwUCUiIiKi8tbfD3R0AG1t8ra/v9gjIqIMGKgSERERUfnq7wf27QMuXAC8Xnm7bx+DVSKLY6BKREREROXr0CHA6QRqagAh5K3TKe8nIstioEpERERE5WtoCKiuTryvuho4e7YowyEifRioEhEREVH5amsDZmcT75udBTZvLspwiEgfBqpEREREpYpFgjLr6QHCYWBmBlAUeRsOy/uJyLIYqBIRERGVIhYJ0qerCzh8GNiwAZiYkLeHD8v7iciyhKIoxR6Dpl27dilHjx4t9jCIiIiIrKmjQwanNTWx+2ZmZCB25EjxxkVEpJMQ4teKouzS+h0zqkRERESliEWCiKiMMVAlIiIiKkUsEkREZYyBKhEREVEpYpEgIipjDFSJiIiIShGLBBFRGXMUewBERERElKWuLgamRFSWmFElIiIiIiIiS2GgSkRERERERJbCQJWIiIiIiIgshYEqERERERERWQoDVSIiIiIiIrIUBqpERERERERkKQxUiYiIiIiIyFIYqBIREREREZGlMFAlIiIiIiIiS2GgSkRERERERJbCQJWIiIiIiIgshYEqERERERERWQoDVSIiIiIiIrIUBqpERERERERkKQxUiYiIiIiIyFIYqBIREREREZGlMFAlIiIiIiIiS2GgSkRERERERJbCQJWIiIiIiIgshYEqERERERERWQoDVSIiIiIiIrIUBqpERERERERkKQxUiYiIiIiIyFIYqBIREREREZGlMFAlIiIiIiIiS2GgSkRERERERJYiFEUp9hg0CSEuAXi/2OOgnDUCuFzsQVBB8ZivPjzmqwuP9+rDY7768JivPsU65psURVmr9QvLBqpUHoQQRxVF2VXscVDh8JivPjzmqwuP9+rDY7768JivPlY85pz6S0RERERERJbCQJWIiIiIiIgshYEq5ds3iz0AKjge89WHx3x14fFefXjMVx8e89XHcseca1SJiIiIiIjIUphRJSIiIiIiIkthoEpERERERESWwkCVTCWE+KwQ4rdCiKgQImWJayHE7wkhTgkhzgghHi7kGMlcQgivEOIlIYR/6bYhxeMWhRBvL/17vtDjpNxk+swKIVxCiP+59PvXhRCbCz9KMpOOY/45IcSluM/1fynGOMk8QognhRCjQojjKX4vhBDfWDonfiOEuKnQYyTz6Djeu4UQU3Gf8S8VeoxkLiHEVUKIHwshTi5dr/8/Go+xzOecgSqZ7TiAzwD4aaoHCCHsAJ4A0AVgB4B7hBA7CjM8yoOHAbysKIoPwMtLP2uZUxTlhqV/dxZueJQrnZ/ZvQAmFEXZAuBrAP6+sKMkMxn4O/0/4z7X3yroICkfvgPg99L8vguAb+nf5wH0FmBMlD/fQfrjDQA/i/uMP1qAMVF+RQD8uaIo2wH8DoD/qvG33TKfcwaqZCpFUU4qinIqw8M+DOCMoiiDiqKEAfwLgLvyPzrKk7sAPLX0308B+E9FHAvlh57PbPx58CyATwkhRAHHSObi3+lVSFGUnwIYT/OQuwB8V5F+CaBeCLGhMKMjs+k43lRmFEW5oCjKm0v/PQ3gJICWpIdZ5nPOQJWKoQXAB3E/n8fKDwmVjiZFUS4A8g8ggHUpHlcphDgqhPilEILBbGnR85ldfoyiKBEAUwDWFGR0lA96/07/4dLUsGeFEP9/e3cTWkcVxmH8+WOim0IRIlRpUyq4EFduROlGpCBkUSi0kE2/0EUEEdd1Ibjqyo0gih+bIoWC0qY0pVCCW1FKS1E31YWEiGKFiihC8XUxNxBiPm64N525yfPb3I+Ze+4LZ16Gd845M/seTGhqkefvneeFJLeSXE3yTNvBaHh6S3SeBb5asakzeT7Wxp9qtCW5DuxZZdNbVXWpnyZW+c7nJHXYen2+iWYmq2oxyZPAfJLbVfXDcCLUFusnZ83r7aWf/rwMnK+qf5LM0Iyov7TlkalN5vnOcgPYX1V/JpkCLtJMB9WIS7IL+Bx4s6r+WLl5lZ+0kucWqtq0qjo0YBMLwPIr73uBxQHb1BZar8+T/JLk8ar6uTc15Nc12ljsvf6Y5Euaq3gWqqOhn5xd2mchyRiwG6eUjbIN+7yq7i77+BGuS94JPH/vIMsLmKqaS/J+komq+q3NuDSYJOM0RepnVfXFKrt0Js+d+qs2fA08leRAkoeBacC7wI6uWeBk7/1J4H+j6kkeTfJI7/0EcBD47oFFqEH1k7PLj4OjwHxVOdIyujbs8xVrlg7TrHXS9jYLnOjdFfR54N7S0g9tP0n2LN1rIMlzNHXD3fV/pS7r9ecnwPdV9e4au3Umzx1R1VAlOQK8BzwGXElys6peTvIE8HFVTVXV/SSvA9eAh4BPq+rbFsPWYM4CF5K8AvwEHANI83iimap6FXga+DDJvzQnurNVZaE6ItbK2STvAN9U1SzNie9ckjs0I6nT7UWsQfXZ528kOUxzF8nfgVOtBayhSHIeeBGYSLIAvA2MA1TVB8AcMAXcAf4CTrcTqYahj/4+CryW5D7wNzDtBciRdxA4DtxOcrP33RlgErqX5/F4kyRJkiR1iVN/JUmSJEmdYqEqSZIkSeoUC1VJkiRJUqdYqEqSJEmSOsVCVZIkSZLUKRaqkiRJkqROsVCVJEmSJHXKfxAQ5r/7V0xRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate edge features for test data\n",
    "edge_data = (edge_data_ids_test, edge_data_labels_test)\n",
    "X, y = test_fl.transform(edge_data, \"l2\")\n",
    "\n",
    "# Learn a projection from 128 dimensions to 2\n",
    "pca = PCA(n_components=2)\n",
    "X_transformed = pca.fit_transform(X)\n",
    "\n",
    "# plot the 2-dimensional points\n",
    "red = y == 0\n",
    "green = y == 1\n",
    "plt.figure(figsize=(16, 12))\n",
    "plt.scatter(X_transformed[red, 0], X_transformed[red, 1], c=\"r\", alpha=0.8)\n",
    "plt.scatter(X_transformed[green, 0], X_transformed[green, 1], c=\"g\", alpha=0.4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example has demonstrated how to use the `stellargraph` library to build a link prediction algorithm for homogeneous graphs using the Node2Vec, [1], representation learning algorithm."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
